Embedded micro-channel heat dissipation system oriented to SIP (Session Initiation Protocol) packaging and collaborative heat management method

Abstract

The invention relates to the technical field of chip heat management, in particular to an embedded micro-channel heat dissipation system oriented to SIP packaging and a collaborative heat management method and system.The method comprises the steps that a heat dissipation channel set is determined, a current working data set of a working chip set is collected, a neural network model is trained through a training chip data set, and a cooling effect prediction model is obtained; iterating the initial particle swarm according to the cooling effect prediction model and the current working data set to obtain an optimal cooling liquid flow rate, sorting the working chip sets in the thermal management system by using the current working data set to obtain a priority chip set, and cooling the priority chip set by using the heat dissipation flow channel set and the optimal cooling liquid flow rate. According to the invention, the precision and the intelligent level of chip thermal management in SIP packaging can be improved.

Description

Technical Field

[0001] The present invention relates to the field of chip thermal management technology, and in particular to an embedded microchannel heat dissipation system for SIP packaging and a collaborative thermal management method and system. Background Art

[0002] In SIP (system-in-package) packaging technology, multiple chips are integrated together, which makes the heat accumulation between the chips more serious. If the heat is not effectively dissipated, it will lead to chip performance degradation, shortened lifespan or even damage. A good thermal management system can not only improve the operating efficiency of the chip, but also reduce the system's energy consumption and extend the service life of the equipment.

[0003] Traditional thermal management methods mainly rely on fixed cooling strategies, such as simply adjusting the coolant flow rate according to the chip temperature, or using a unified heat dissipation solution to dissipate heat for all chips. Traditional methods cannot dynamically adjust the coolant flow rate according to the actual power consumption and load of the chip, which may cause energy waste. In addition, traditional methods lack refined management of the heat dissipation priority of different chips, and cannot effectively prioritize heat dissipation for chips with higher temperatures, power consumption and load, thereby affecting the heat dissipation efficiency of the entire system. Summary of the Invention

[0004] The present invention provides an embedded microchannel heat dissipation system and a collaborative thermal management method and system for SIP packaging, the main purpose of which is to improve the accuracy and intelligence level of chip thermal management in SIP packaging.

[0005] To achieve the above objectives, the present invention provides an embedded microchannel heat dissipation system and a collaborative thermal management method for SIP packaging, comprising:

[0006] receiving a thermal management instruction, and determining a thermal management system based on the thermal management instruction, wherein the thermal management system includes a working chipset, an embedded micro-channel, and a cooling pump, and the working chipset includes a plurality of working chips;

[0007] Determine the heat dissipation channel of each working chip in the working chip group based on the embedded micro-channel to obtain a heat dissipation channel group, wherein the heat dissipation channels in the heat dissipation channel group are all installed with micro electromagnetic valves;

[0008] Collecting current working data of each working chip in the working chipset to obtain a current working data group, wherein the current working data includes: current chip temperature, current chip power consumption and current chip load;

[0009] Obtaining a training chip data set, and using the training chip data set to train a pre-built neural network model to obtain a cooling effect prediction model, wherein the training chip data in the training chip data set are all labeled;

[0010] Initializing a pre-constructed particle swarm based on a preset flow rate constraint range to obtain an initial particle swarm, wherein the initial particle swarm includes a plurality of initial particles, and positions of the initial particles are the coolant flow rate of the cooling pump;

[0011] Iterating the initial particle swarm according to the cooling effect prediction model and the current working data set to obtain an optimal coolant flow rate;

[0012] Using the current working data group, calculate the cooling priority of the working chipsets in the thermal management system to obtain a chip cooling weight group, and sort the working chipsets based on the chip cooling weight group to obtain a priority chipset;

[0013] The heat dissipation channel group and the optimal coolant flow rate are used to cool the priority chipset, completing the embedded microchannel heat dissipation system and collaborative thermal management for SIP packaging.

[0014] Optionally, obtaining a training chip data set includes:

[0015] Constructing a test thermal management system, wherein the test thermal management system includes: a test chipset, a test micro-channel and a test cooling pump;

[0016] Extracting test chips from a test chip group of a test thermal management system in sequence, and collecting test data on the test chips to obtain a test chip data set, wherein the test chip data set includes a plurality of test chip data, and the test chip data includes: test chip temperature, test chip power consumption, test chip load, and test coolant flow rate;

[0017] Each test chip data in the test chip data set is labeled to obtain a labeled chip data set;

[0018] The labeled chip data sets corresponding to each test chip in the test chip set are merged to obtain a training chip data set.

[0019] Optionally, labeling each test chip data in the test chip data set to obtain a labeled chip data set includes:

[0020] Extracting test chip data in sequence from the test chip data set, and determining whether the test chip temperature in the test chip data is greater than a preset cooling temperature threshold;

[0021] If the test chip temperature in the test chip data is greater than the cooling temperature threshold, the test chip data is marked with a preset invalid cooling tag to obtain marked chip data;

[0022] If the test chip temperature in the test chip data is not greater than the cooling temperature threshold, determining the test time of the test chip data;

[0023] Determine valid chip data in the test chip data set based on the test time and the preset effective cooling time, and judge whether the test chip temperature in the valid chip data is greater than the cooling temperature threshold;

[0024] If the temperature of the test chip in the valid chip data is not greater than the cooling temperature threshold, the test chip data is marked with a preset valid cooling label to obtain marked chip data;

[0025] If the test chip temperature in the valid chip data is greater than the cooling temperature threshold, the test chip data is marked with an invalid cooling tag to obtain marked chip data;

[0026] The labeled chip data corresponding to each test chip data are summarized to obtain the labeled chip data set.

[0027] Optionally, iterating the initial particle swarm according to the cooling effect prediction model and the current working data set to obtain the optimal coolant flow rate includes:

[0028] Sequentially extracting initial particles from the initial particle group, determining initial positions of the initial particles, and calculating initial fitness of the initial particles according to the initial positions and a cooling effect prediction model;

[0029] Summarizing the initial positions and initial fitnesses respectively to obtain an initial position set and an initial fitness set, and determining an optimal initial fitness in the initial fitness set;

[0030] Based on the initial position set, the initial fitness set and the optimal initial fitness, the initial particle swarm is iterated until the change rate of the optimal initial fitness is no greater than the preset standard change rate, and the optimal initial fitness in the last iteration is recorded as the optimal population fitness;

[0031] The optimal particle position corresponding to the optimal population fitness is determined, and the optimal coolant flow rate is determined based on the optimal particle position.

[0032] Optionally, the calculating the initial fitness of the initial particles according to the initial position and cooling effect prediction model includes:

[0033] Extracting working chips in the working chip group in sequence, and determining working chip data corresponding to the working chips in the current chip data group, wherein the working chip data includes: working chip temperature, working chip power consumption, and working chip load;

[0034] determining an initial coolant flow rate corresponding to the initial position;

[0035] Inputting the working chip temperature, working chip power consumption, working chip load and initial coolant flow rate into the cooling effect prediction model to obtain a cooling effect value;

[0036] Summarizing the cooling effect value corresponding to each working chip in the working chip group to obtain a cooling effect value group, and calculating the comprehensive effect value based on the cooling effect value group;

[0037] Based on the initial coolant flow rate, performing a system balance analysis on the thermal management system to obtain a balance parameter group, wherein the balance parameter group includes: a cooling energy consumption value, a cooling pressure value, and a system wear value;

[0038] Calculate the system balance value according to the balance parameter group;

[0039] Based on the system balance value and the comprehensive effect value, the initial fitness of the initial particles is generated.

[0040] Optionally, calculating the comprehensive effect value according to the cooling effect value group includes:

[0041] Identify the maximum effect value in the cooling effect value group, and determine whether the maximum effect value is greater than a preset standard effect value;

[0042] If the maximum effect value is not greater than the standard effect value, the comprehensive effect value is recorded as the preset zero value;

[0043] If the maximum effect value is greater than the standard effect value, the cooling effect value group is averaged to obtain the comprehensive effect value.

[0044] Optionally, performing a system balance analysis on the thermal management system based on the initial coolant flow rate to obtain a balance parameter group includes:

[0045] Determining a system pressure drop according to a preset system resistance coefficient and the initial coolant flow rate, and recording the system pressure drop as a cooling pressure value;

[0046] Obtaining a microchannel cross-sectional area of ​​the embedded microchannel, determining a cooling energy consumption power based on the microchannel cross-sectional area, an initial coolant flow rate, a system pressure drop, and a cooling pump, and recording the cooling energy consumption power as a cooling energy consumption value;

[0047] Acquire a historical wear data set of the embedded microfluidic channel, determine a wear rate based on the historical wear data set, and record the wear rate as a system wear value, wherein the historical wear data set includes a plurality of historical wear data;

[0048] The cooling pressure value, cooling energy consumption value and system wear value are summarized to obtain a balance parameter group.

[0049] Optionally, calculating the system balance value according to the balance parameter group includes:

[0050] Obtaining a baseline coolant flow rate of the embedded microchannel, and determining a baseline balance parameter group based on the baseline coolant flow rate, wherein the baseline balance parameter group includes: a baseline cooling energy consumption value, a baseline cooling pressure value, and a baseline system wear value;

[0051] The system balance value is calculated based on the baseline balance parameter group and the balance parameter group, where the system balance value is expressed as:

[0052]

[0053] Wherein, T represents the system balance value, n represents the number of reference balance parameters in the reference balance parameter group or the number of balance parameters in the balance parameter group, represents the ith balance parameter in the balance parameter group, represents the i-th benchmark balance parameter in the benchmark balance parameter group, β i Represents the parameter weight coefficient of the preset i-th balance parameter.

[0054] Optionally, the step of calculating the cooling priority of the working chipsets in the thermal management system using the current working data set to obtain a chip cooling weight set includes:

[0055] Set the high temperature weight range and normal temperature weight range. The high temperature weight range is the numerical range of the chip cooling weight when the current chip temperature is less than the cooling temperature threshold, and the normal temperature weight range is the numerical range of the chip cooling weight when the current chip temperature is not less than the cooling temperature threshold.

[0056] Extracting current working data in the current working data group in sequence;

[0057] Construct temperature impact weights based on current chip power consumption and current chip load in current working data;

[0058] Based on the temperature impact weight, high temperature weight range, normal temperature weight range, and the current chip temperature in the current working data, the chip cooling weight of the working chip corresponding to the current working data in the working chipset is calculated. The chip cooling weight is expressed as:

[0059]

[0060] Among them, q represents the chip cooling weight, k0 represents the minimum value in the normal temperature weight range, k1 represents the maximum value in the normal temperature weight range or the minimum value in the high temperature weight range, T represents the current chip temperature, T b represents the cooling temperature threshold, and k2 represents the maximum value in the high temperature weight range;

[0061] The chip cooling weights are aggregated to obtain a chip cooling weight group.

[0062] To achieve the above objectives, the present invention further provides an embedded microchannel heat dissipation system and a collaborative thermal management system for SIP packaging, comprising:

[0063] a heat dissipation channel division module, configured to receive a thermal management instruction and determine a thermal management system based on the thermal management instruction, wherein the thermal management system includes a working chipset, an embedded micro-channel, and a cooling pump, and the working chipset includes multiple working chips; the heat dissipation channel of each working chip in the working chipset is determined based on the embedded micro-channel to obtain a heat dissipation channel group, wherein each heat dissipation channel in the heat dissipation channel group is equipped with a micro-electromagnetic valve;

[0064] A prediction model training module is used to collect current working data of each working chip in the working chipset to obtain a current working data group, wherein the current working data includes: current chip temperature, current chip power consumption and current chip load, obtain a training chip data set, and use the training chip data set to train a pre-built neural network model to obtain a cooling effect prediction model, wherein the training chip data in the training chip data set are all labeled;

[0065] An optimal flow rate calculation module is used to initialize a pre-constructed particle swarm based on a preset flow rate constraint range to obtain an initial particle swarm, wherein the initial particle swarm includes multiple initial particles, and the positions of the initial particles are the coolant flow rate of the cooling pump. The initial particle swarm is iterated based on the cooling effect prediction model and the current working data set to obtain the optimal coolant flow rate;

[0066] The priority chip cooling module is used to use the current working data group to calculate the cooling priority of the working chipset in the thermal management system, obtain the chip cooling weight group, sort the working chipset based on the chip cooling weight group, obtain the priority chipset, and use the heat dissipation channel group and the optimal coolant flow rate to cool the priority chipset.

[0067] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0068] a memory storing at least one instruction; and

[0069] The processor executes the instructions stored in the memory to implement the above-mentioned embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging.

[0070] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging.

[0071] In order to solve the problems described in the background technology, the present invention first determines a special heat dissipation channel for each working chip and installs a micro-electromagnetic valve in the heat dissipation channel group, which can realize the refined control of the heat dissipation of each chip, improve the heat dissipation efficiency and flexibility, and ensure that the coolant can accurately flow to each working chip for cooling as needed. Then, a cooling effect prediction model is obtained through training, which can scientifically predict the cooling effect based on the chip's working data and the coolant flow rate, and provide a decision-making basis for the subsequent determination of the optimal coolant flow rate, thereby improving the intelligence level and accuracy of the heat dissipation control and avoiding the limitations of the traditional single temperature monitoring cooling strategy. Furthermore, according to the cooling effect prediction model and the current working data group, the initial particle group is iterated to obtain the optimal coolant flow rate. The cooling effect and system balance are comprehensively considered through the iterative process, and the optimal coolant flow rate can be accurately determined, which can not only effectively reduce the chip temperature, but also avoid The system solves the problems of increased energy consumption, excessive pressure and microchannel wear caused by excessive flow rate, and achieves the best balance between heat dissipation effect and system stability. Then, the current working data group is used to calculate the cooling priority of the working chipset in the thermal management system, and the chip cooling weight group is obtained. The working chipset is sorted based on the chip cooling weight group to obtain the priority chipset. The heat dissipation resources can be reasonably allocated according to the current operating state of the chip, and the chips with higher temperature, power consumption and load are given priority for heat dissipation, thereby improving the heat dissipation efficiency of the entire system, ensuring the stable operation of key chips, and optimizing the utilization of heat dissipation resources. Finally, by combining the heat dissipation channel group and the optimal coolant flow rate, the priority chipset is targeted for cooling and collaborative thermal management is achieved, which can give full play to the advantages of the embedded microchannel heat dissipation system, efficiently reduce the chip temperature, improve the performance and reliability of the entire SIP packaging system, and achieve efficient collaborative operation of the heat dissipation system. Therefore, the present invention can improve the accuracy and intelligence level of chip thermal management in SIP packaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A schematic diagram of a flow chart of an embedded microchannel heat dissipation system and a collaborative thermal management method for SIP packaging provided by one embodiment of the present invention;

[0073] Figure 2 A functional module diagram of an embedded microchannel heat dissipation system and a collaborative thermal management system for SIP packaging provided by one embodiment of the present invention;

[0074] Figure 3 A schematic structural diagram of an electronic device implementing the embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging provided by an embodiment of the present invention;

[0075] Figure 4A schematic structural diagram of a thermal management system for implementing the embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging provided by one embodiment of the present invention.

[0076] Description of reference numerals:

[0077] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0078] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0079] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0080] The embodiments of the present application provide an embedded microfluidic heat dissipation system and a collaborative thermal management method for SIP packaging. The execution subject of the embedded microfluidic heat dissipation system and the collaborative thermal management method for SIP packaging includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiments of the present application. In other words, the embedded microfluidic heat dissipation system and the collaborative thermal management method for SIP packaging can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0081] Reference Figure 1 FIG2 is a flow chart of an embedded microfluidic heat dissipation system and a collaborative thermal management method for SIP packaging provided by an embodiment of the present invention. In this embodiment, the embedded microfluidic heat dissipation system and the collaborative thermal management method for SIP packaging include:

[0082] S1. Receive a thermal management instruction, and determine a thermal management system based on the thermal management instruction, wherein the thermal management system includes a working chipset, an embedded micro-channel, and a cooling pump, and the working chipset includes multiple working chips.

[0083] It is understood that the thermal management instruction refers to an instruction initiated by humans to perform thermal management on a specific system, and the thermal management system refers to the specific system indicated in the thermal management instruction. The thermal management system can refer to Figure 4. The working chipset refers to a combination of multiple working chips, wherein the working chip refers to a semiconductor chip (such as a CPU, GPU or AI accelerator) that performs computing tasks in a SIP package. The embedded microchannel refers to a network of micro coolant circulation pipes integrated inside the chip substrate. The cooling pump refers to an electromechanical device that drives the circulation of the coolant. The cooling pump can cool the working chips in the working chipset through the coolant, and the flow rate of the coolant flowing in the embedded microchannel can be controlled by the cooling pump.

[0084] For example, in a SIP package module of a data center server, the working chipset includes 1 CPU and 4 GPUs; the embedded microfluidic channel is a serpentine microchannel etched in the silicon substrate; the cooling pump is connected to the microfluidic channel inlet and controls the flow rate of the coolant by adjusting the rotation speed, wherein the coolant is, for example, water or ethylene glycol solution.

[0085] S2. Determine the heat dissipation channel of each working chip in the working chip group based on the embedded microchannel to obtain a heat dissipation channel group, wherein the heat dissipation channels in the heat dissipation channel group are all installed with micro solenoid valves.

[0086] It is clear that the heat dissipation channel refers to the area in the embedded microchannel used to cool the working chip. The micro solenoid valve refers to an electrically controlled valve installed at the branch of the heat dissipation channel, which is used to change the flow direction of the coolant in the heat dissipation channel.

[0087] S3. Collect current working data of each working chip in the working chip group to obtain a current working data group, wherein the current working data includes: current chip temperature, current chip power consumption and current chip load.

[0088] It should be explained that the current working data refers to the combination of the current working data corresponding to all working chips. The current working data refers to the combination of various parameters of the working chip during operation, wherein the current working data includes the current chip temperature, the current chip power consumption and the current chip load. The current chip temperature refers to the real-time temperature value of the chip surface, and the current chip temperature is obtained by collecting it through a temperature sensor integrated in the chip. The current chip power consumption refers to the electric power currently consumed by the chip, and the current chip power consumption is obtained by obtaining it through a power meter or a power consumption monitoring unit built into the chip. The current chip load refers to the utilization rate of the chip computing resources, and the current chip load is obtained by reading it through the operating system or hardware performance counter.

[0089] S4. Obtain a training chip data set, and use the training chip data set to train a pre-built neural network model to obtain a cooling effect prediction model, wherein the training chip data in the training chip data set are all labeled.

[0090] It is understood that the training chip data set refers to a data set used for supervised training of a neural network model. The neural network model refers to a structure such as a multi-layer perceptron (MLP) or a convolutional neural network (CNN), for example, an MLP model comprising an input layer (4 neurons: temperature, power consumption, load, flow rate), a hidden layer (10 neurons), and an output layer (1 neuron). The cooling effect prediction model refers to a trained neural network model, the input of which is the current working data of the working chip under a certain data collection and the flow rate of the coolant, and the output is a probability value, which represents the probability of effectively cooling the working chip at the input flow rate of the coolant, wherein effective cooling refers to reducing the current chip temperature of the working chip to below the cooling temperature threshold within a preset effective cooling time.

[0091] In detail, obtaining a training chip data set includes:

[0092] Constructing a test thermal management system, wherein the test thermal management system includes: a test chipset, a test micro-channel and a test cooling pump;

[0093] Extracting test chips from a test chip group of a test thermal management system in sequence, and collecting test data on the test chips to obtain a test chip data set, wherein the test chip data set includes a plurality of test chip data, and the test chip data includes: test chip temperature, test chip power consumption, test chip load, and test coolant flow rate;

[0094] Each test chip data in the test chip data set is labeled to obtain a labeled chip data set;

[0095] The labeled chip data sets corresponding to each test chip in the test chip set are merged to obtain a training chip data set.

[0096] It is understood that the test thermal management system refers to a thermal management system used to conduct tests, wherein the test chipset, test microchannel, and test cooling pump refer to the working chipset, embedded microchannel, and cooling pump in the test thermal management system, respectively. The test chip data set refers to a collection of test chip data collected at different times of the test chip, wherein the test chip temperature, test chip power consumption, and test chip load refer to the current chip temperature, current chip power consumption, and current chip load in the test chip data, respectively. The test coolant flow rate refers to the flow rate of the coolant in the test microchannel when the current chip temperature of the test chip is collected. The labeled chip data refers to the labeled test chip data.

[0097] Furthermore, the merging of the label chip data sets corresponding to each test chip in the test chip group refers to: merging the test chip data in multiple test chip data sets into the same array, and the merged array is the training chip data set. For example, there are two label chip data sets: label chip data set 1: {data A, data B}, label chip data set 2: {data C, data D}, then after merging these two label chip data sets, the training chip data set obtained is: {data A, data B, data C, data D}.

[0098] Understandably, for test chips with high power consumption, even if the chip temperature does not exceed the preset cooling threshold, there is a high probability that the chip temperature will rise above the cooling threshold during subsequent operation. Therefore, it is also necessary to perform early cooling for test chips with high power consumption. Similarly, a high test chip load will cause the chip temperature to rise. Therefore, for test chips with high test chip load, even if the chip temperature does not reach the preset cooling threshold, coolant is still required to prevent the chip from subsequently overheating due to the high load.

[0099] Furthermore, through deep learning, we can learn the impact of chip power consumption and chip load on the cooling effect of coolant flow rate, thereby avoiding the limitation of formulating cooling strategies based only on the single dimension of chip temperature.

[0100] Specifically, each test chip data in the test chip data set is labeled to obtain a labeled chip data set, including:

[0101] Extracting test chip data in sequence from the test chip data set, and determining whether the test chip temperature in the test chip data is greater than a preset cooling temperature threshold;

[0102] If the test chip temperature in the test chip data is greater than the cooling temperature threshold, the test chip data is marked with a preset invalid cooling tag to obtain marked chip data;

[0103] If the test chip temperature in the test chip data is not greater than the cooling temperature threshold, determining the test time of the test chip data;

[0104] Determine valid chip data in the test chip data set based on the test time and the preset effective cooling time, and judge whether the test chip temperature in the valid chip data is greater than the cooling temperature threshold;

[0105] If the temperature of the test chip in the valid chip data is not greater than the cooling temperature threshold, the test chip data is marked with a preset valid cooling label to obtain marked chip data;

[0106] If the test chip temperature in the valid chip data is greater than the cooling temperature threshold, the test chip data is marked with an invalid cooling tag to obtain marked chip data;

[0107] The labeled chip data corresponding to each test chip data are summarized to obtain the labeled chip data set.

[0108] It is clear that the cooling temperature threshold refers to a temperature constant set manually. When the surface temperature of the chip exceeds the cooling temperature threshold, it indicates that the chip needs to be cooled. The invalid cooling label refers to a numerical label (such as 0) indicating "cooling failure". The use of the invalid cooling label to mark the test chip data refers to writing the invalid cooling label into the field corresponding to the test chip data. The test time refers to the moment when the test chip data is obtained. The effective cooling duration refers to a time constant set manually. The effective termination time can be obtained through the effective cooling duration and the test time. The effective termination time is the time after the test time is added to the effective cooling duration. The effective chip data refers to the test chip data collected at the effective termination time. The effective cooling label refers to the numerical label of "cooling success".

[0109] It should be explained that in order to obtain the effect of the coolant flow rate on the temperature of the test chip under different working conditions, the flow rate of the coolant in the test microchannel can be changed in real time during the step of collecting test data on the test chip to obtain the test chip data set. Optionally, the change in the flow rate of the coolant in the test microchannel can follow the following formula: V s =V0+ΔV s , where V s represents the flow rate of the coolant in the test microchannel, V0 represents the artificially set reference flow rate, ΔV s A constant representing an artificially set change in flow rate.

[0110] Furthermore, since both chip power consumption and chip load will affect the temperature of the chip, for a certain test chip data, even if the test chip temperature in the test chip data is not greater than the cooling temperature threshold, it is necessary to determine whether the test chip data will cause the corresponding chip temperature to exceed the cooling temperature threshold due to the influence of chip power consumption and chip load. Therefore, the effective cooling time is introduced. If after the effective cooling time, the test chip temperature in the effective chip data is still not greater than the cooling temperature threshold, it can be explained that the current coolant flow rate can achieve the purpose of cooling, that is, effective cooling.

[0111] S5. Initialize the pre-constructed particle swarm based on a preset flow rate constraint range to obtain an initial particle swarm, wherein the initial particle swarm includes a plurality of initial particles, and the positions of the initial particles are the coolant flow rate of the cooling pump.

[0112] It is clear that the flow rate constraint range refers to the manually set control range of the coolant flow rate in the cooling pump, for example: 0.1m / s to 5.0m / s. The particle swarm refers to the set of candidate coolant flow rate solutions, and the initial particle swarm refers to the initialized particle swarm, where initialization refers to the initialization of the position and velocity of each particle in the particle swarm. It should be noted that the initial particles need to correspond to an initial fitness, which is not obtained in the initialization step, but is obtained based on the position of the initial particles.

[0113] It should be explained that the initial fitness refers to a numerical value that quantifies the degree of cooling of the initial particles at the initial position (i.e., at the initial coolant flow rate). When the coolant flow rate increases, the degree of cooling for the chip is better, but a higher coolant flow rate will cause the balance of the thermal management system to be destroyed. For example, as the coolant flow rate increases, the energy consumption of the cooling pump used to control the coolant in the thermal management system will increase, and the pressure of the coolant will increase. At the same time, the greater the coolant flow rate, the greater the wear on the embedded microchannels. In order to quantify these balance disruptions caused by the increase in coolant flow rate, the system balance value is introduced. Furthermore, the purpose of the above initial fitness is to ensure that the system balance value is within a controllable range while controlling the chip temperature of all current chips in the current chipset.

[0114] S6. Iterate the initial particle group according to the cooling effect prediction model and the current working data group to obtain the optimal coolant flow rate.

[0115] It can be understood that the optimal coolant flow rate refers to the coolant flow rate that has the best cooling effect after the particle swarm is iterated.

[0116] In detail, the iterative process of the initial particle swarm based on the cooling effect prediction model and the current working data set to obtain the optimal coolant flow rate includes:

[0117] Sequentially extracting initial particles from the initial particle group, determining initial positions of the initial particles, and calculating initial fitness of the initial particles according to the initial positions and a cooling effect prediction model;

[0118] Summarizing the initial positions and initial fitnesses respectively to obtain an initial position set and an initial fitness set, and determining an optimal initial fitness in the initial fitness set;

[0119] Based on the initial position set, the initial fitness set and the optimal initial fitness, the initial particle swarm is iterated until the change rate of the optimal initial fitness is no greater than the preset standard change rate, and the optimal initial fitness in the last iteration is recorded as the optimal population fitness;

[0120] The optimal particle position corresponding to the optimal population fitness is determined, and the optimal coolant flow rate is determined based on the optimal particle position.

[0121] It will be understood that the initial position refers to the coolant flow rate corresponding to the initial particle. The optimal initial fitness refers to the initial fitness with the largest value in the initial fitness set. Iterating the initial particle swarm is a common step in the particle swarm optimization algorithm and will not be repeated here. The standard rate of change refers to a manually set constant. The optimal particle position refers to the particle position corresponding to the optimal population fitness, and the optimal coolant flow rate refers to the coolant flow rate corresponding to the optimal particle position.

[0122] In detail, the calculation of the initial fitness of the initial particles according to the initial position and cooling effect prediction model includes:

[0123] Extracting working chips in the working chip group in sequence, and determining working chip data corresponding to the working chips in the current chip data group, wherein the working chip data includes: working chip temperature, working chip power consumption, and working chip load;

[0124] determining an initial coolant flow rate corresponding to the initial position;

[0125] Inputting the working chip temperature, working chip power consumption, working chip load and initial coolant flow rate into the cooling effect prediction model to obtain a cooling effect value;

[0126] Summarizing the cooling effect value corresponding to each working chip in the working chip group to obtain a cooling effect value group, and calculating the comprehensive effect value based on the cooling effect value group;

[0127] Based on the initial coolant flow rate, performing a system balance analysis on the thermal management system to obtain a balance parameter group, wherein the balance parameter group includes: a cooling energy consumption value, a cooling pressure value, and a system wear value;

[0128] Calculate the system balance value according to the balance parameter group;

[0129] Based on the system balance value and the comprehensive effect value, the initial fitness of the initial particles is generated.

[0130] It should be explained that the working chip data refers to the current chip data corresponding to the working chip, and the working chip temperature, working chip power consumption, and working chip load refer to the current chip temperature, current chip power consumption, and current chip load corresponding to the working chip data, respectively. The initial coolant flow rate refers to the coolant flow rate at the initial position. The cooling effect value refers to the output value of the cooling effect prediction model, which is a probability value. The comprehensive effect value refers to a numerical value that quantifies the cooling effect of the initial coolant flow rate on the working chip. The larger the comprehensive effect value, the better the cooling effect of the initial coolant flow rate on the working chip.

[0131] It is understood that the cooling energy consumption value refers to the power consumed by the cooling pump (unit: watts), the cooling pressure value refers to the pressure drop of the coolant in the microchannel (unit: Pascals), and the system wear value refers to the wear thickness of the microchannel per unit time (unit: μm / h). The system balance value quantifies the degree to which the initial coolant flow rate disrupts the balance of the thermal management system. The smaller the system balance value, the less disruption the initial coolant flow rate has on the balance of the thermal management system.

[0132] Furthermore, the calculation method of the initial fitness can be selected as: F=b1×1 / T+b2×S, where F represents the initial fitness, b1 represents the preset balance weight, which can be set to 0.4, b2 represents the preset cooling weight, which can be set to 0.6, and S represents the comprehensive effect value.

[0133] In detail, the calculating of the comprehensive effect value according to the cooling effect value group includes:

[0134] Identify the maximum effect value in the cooling effect value group, and determine whether the maximum effect value is greater than a preset standard effect value;

[0135] If the maximum effect value is not greater than the standard effect value, the comprehensive effect value is recorded as the preset zero value;

[0136] If the maximum effect value is greater than the standard effect value, the cooling effect value group is averaged to obtain the comprehensive effect value.

[0137] It is understood that the maximum effect value refers to the cooling effect value with the largest value in the cooling effect value group. The standard effect value refers to a manually set constant related to the cooling effect value. The average calculation refers to calculating the average of all cooling effect values ​​in the cooling effect value group and using the average as the comprehensive effect value.

[0138] Specifically, based on the initial coolant flow rate, the thermal management system is subjected to a system balance analysis to obtain a balance parameter group, including:

[0139] Determining a system pressure drop according to a preset system resistance coefficient and the initial coolant flow rate, and recording the system pressure drop as a cooling pressure value;

[0140] Obtaining a microchannel cross-sectional area of ​​the embedded microchannel, determining a cooling energy consumption power based on the microchannel cross-sectional area, an initial coolant flow rate, a system pressure drop, and a cooling pump, and recording the cooling energy consumption power as a cooling energy consumption value;

[0141] Acquire a historical wear data set of the embedded microfluidic channel, determine a wear rate based on the historical wear data set, and record the wear rate as a system wear value, wherein the historical wear data set includes a plurality of historical wear data;

[0142] The cooling pressure value, cooling energy consumption value and system wear value are summarized to obtain a balance parameter group.

[0143] It should be explained that the system pressure drop refers to the pressure loss when the coolant flows through the microchannel. The greater the pressure drop, the greater the fluid resistance and the higher the system energy consumption. The pressure drop calculation formula is: ΔP = K × V 2 , where ΔP represents the system pressure drop, K represents the preset system resistance coefficient, and V represents the initial coolant flow rate. The system resistance coefficient is a manually set constant and can be set to: 120 Pa·s 2 / m 2 . The cooling energy consumption power refers to the power required by the cooling pump to maintain the flow rate. The greater the cooling energy consumption power, the lower the energy efficiency of the cooling pump. The cooling energy consumption power is expressed as: P = α × ΔP × A × V, wherein P represents the cooling energy consumption power, α represents the energy conversion efficiency of the cooling pump, and A represents the cross-sectional area of ​​the microchannel. The energy conversion efficiency of the cooling pump refers to the ratio of the pump converting electrical energy into fluid mechanical energy. The energy conversion efficiency is obtained by referring to the technical specifications of the cooling pump. The historical wear data set refers to the historical wear data under different coolant flow rates, and the historical wear data refers to the historical wear rate and historical coolant flow rate of the embedded microchannel in previous periods. The historical wear thickness refers to the thickness of the embedded microchannel worn per unit time of the historical coolant flow rate. The wear rate refers to the thickness of the embedded microchannel worn per unit time at the coolant flow rate. The calculation formula for the wear rate is: W = a × V b , wherein W represents the wear rate, a and represent the preset first wear coefficient and second wear coefficient respectively, wherein the first wear coefficient and the second wear coefficient are related to the material used to prepare the embedded microfluidic channel, and the first wear coefficient and the second wear coefficient can be obtained by fitting the historical wear data set. Furthermore, determining the wear rate based on the historical wear data set refers to obtaining the wear rate by fitting the historical wear data set, for example: using the least squares method to fit the historical data to obtain a wear rate model with a=0.15 and b=1.8.

[0144] In detail, the calculation of the system balance value according to the balance parameter group includes:

[0145] Obtaining a baseline coolant flow rate of the embedded microchannel, and determining a baseline balance parameter group based on the baseline coolant flow rate, wherein the baseline balance parameter group includes: a baseline cooling energy consumption value, a baseline cooling pressure value, and a baseline system wear value;

[0146] The system balance value is calculated based on the baseline balance parameter group and the balance parameter group, where the system balance value is expressed as:

[0147]

[0148] Wherein, T represents the system balance value, n represents the number of reference balance parameters in the reference balance parameter group or the number of balance parameters in the balance parameter group, represents the ith balance parameter in the balance parameter group, represents the i-th benchmark balance parameter in the benchmark balance parameter group, β i Represents the parameter weight coefficient of the preset i-th balance parameter.

[0149] It is understood that the baseline coolant flow rate refers to the coolant flow rate obtained after the most recent adjustment of the coolant flow rate, and the baseline balance parameter group refers to the balance parameter group at the time of the most recent adjustment of the coolant flow rate. The baseline cooling energy consumption value, baseline cooling pressure value, and baseline system wear value refer to the cooling energy consumption value, cooling pressure value, and system wear value in the baseline balance parameter group, respectively. The parameter weight coefficient of the i-th balance parameter refers to a manually set constant. For example, if the i-th balance parameter is the cooling energy consumption value, if you wish to assign a higher weight to the cooling energy consumption value, you can set the parameter weight coefficient corresponding to the cooling energy consumption value to 0.6.

[0150] S7. Calculate cooling priorities for the working chipsets in the thermal management system using the current working data set to obtain a chip cooling weight set, and sort the working chipsets based on the chip cooling weight set to obtain a priority chipset.

[0151] It is understood that the chip cooling weight group refers to the combination of chip cooling weights corresponding to each working chip, wherein the chip cooling weight refers to the numerical value of the importance of the corresponding working chip in subsequent management. The higher the chip cooling weight, the more important the working chip is. The priority chipset refers to the sorted working chipset, wherein working chips with higher chip cooling weights are ranked higher in the priority chipset.

[0152] Specifically, the method of calculating the cooling priority of the working chipsets in the thermal management system using the current working data set to obtain the chip cooling weight set includes:

[0153] Set the high temperature weight range and normal temperature weight range. The high temperature weight range is the numerical range of the chip cooling weight when the current chip temperature is less than the cooling temperature threshold, and the normal temperature weight range is the numerical range of the chip cooling weight when the current chip temperature is not less than the cooling temperature threshold.

[0154] Extracting current working data in the current working data group in sequence;

[0155] Construct temperature impact weights based on current chip power consumption and current chip load in current working data;

[0156] Based on the temperature impact weight, high temperature weight range, normal temperature weight range, and the current chip temperature in the current working data, the chip cooling weight of the working chip corresponding to the current working data in the working chipset is calculated. The chip cooling weight is expressed as:

[0157]

[0158] Among them, q represents the chip cooling weight, k0 represents the minimum value in the normal temperature weight range, k1 represents the maximum value in the normal temperature weight range or the minimum value in the high temperature weight range, T represents the current chip temperature, T b represents the cooling temperature threshold, and k2 represents the maximum value in the high temperature weight range;

[0159] The chip cooling weights are aggregated to obtain a chip cooling weight group.

[0160] As you can see, the high-temperature weight range and normal-temperature weight range are manually set to ensure that the subsequent chip cooling weight calculation process is dominated by the current chip temperature and is not significantly affected by the current chip power consumption and current chip load. The temperature impact weight quantifies the degree to which the current chip power consumption and current chip load affect the subsequent operating temperature of the working chip. A larger temperature impact weight indicates a greater impact.

[0161] Furthermore, when considering the chip cooling weight, the current chip temperature should be dominant, but the interference of the current chip power consumption and the current chip load on the current chip temperature still needs to be considered. Therefore, the temperature impact weight is introduced, which is expressed as: Among them, q' represents the temperature influence weight, w1 and w2 represent the manually set power consumption weight and load weight respectively. Optionally, w1 and w2 are set to 0.1 and 0.2 respectively, H represents the current chip power consumption, G represents the current chip load, H max Indicates the current chip power consumption with the largest value in the current working data group, G max Indicates the current chip load with the largest value in the current working data group.

[0162] S8. Use the heat dissipation channel group and the optimal coolant flow rate to cool the priority chipset, completing the embedded microchannel heat dissipation system and collaborative thermal management for SIP packaging.

[0163] It needs to be explained that cooling the priority chipset means: controlling the flow rate of the coolant in the embedded microchannel to the optimal coolant flow rate through the cooling pump in the thermal management system, and allowing the coolant in the embedded microchannel to reach the highest priority working chip in the priority chipset first through the closing control of each micro-solenoid valve in the heat dissipation channel group. For example: if the chip with the highest priority is GPU1, the solenoid valves leading to other chips (such as valves 2 / 3 / 4) are closed, and only the solenoid valve of the heat dissipation channel corresponding to GPU1 is opened, so that the coolant flows through the GPU1 area first and then flows to other chips.

[0164] In order to solve the problems described in the background technology, the present invention first determines a special heat dissipation channel for each working chip and installs a micro-electromagnetic valve in the heat dissipation channel group, which can realize the refined control of the heat dissipation of each chip, improve the heat dissipation efficiency and flexibility, and ensure that the coolant can accurately flow to each working chip for cooling as needed. Then, a cooling effect prediction model is obtained through training, which can scientifically predict the cooling effect based on the chip's working data and the coolant flow rate, and provide a decision-making basis for the subsequent determination of the optimal coolant flow rate, thereby improving the intelligence level and accuracy of the heat dissipation control and avoiding the limitations of the traditional single temperature monitoring cooling strategy. Furthermore, according to the cooling effect prediction model and the current working data group, the initial particle group is iterated to obtain the optimal coolant flow rate. The cooling effect and system balance are comprehensively considered through the iterative process, and the optimal coolant flow rate can be accurately determined, which can not only effectively reduce the chip temperature, but also avoid The system solves the problems of increased energy consumption, excessive pressure and microchannel wear caused by excessive flow rate, and achieves the best balance between heat dissipation effect and system stability. Then, the current working data group is used to calculate the cooling priority of the working chipset in the thermal management system, and the chip cooling weight group is obtained. The working chipset is sorted based on the chip cooling weight group to obtain the priority chipset. The heat dissipation resources can be reasonably allocated according to the current operating state of the chip, and the chips with higher temperature, power consumption and load are given priority for heat dissipation, thereby improving the heat dissipation efficiency of the entire system, ensuring the stable operation of key chips, and optimizing the utilization of heat dissipation resources. Finally, by combining the heat dissipation channel group and the optimal coolant flow rate, the priority chipset is targeted for cooling and collaborative thermal management is achieved, which can give full play to the advantages of the embedded microchannel heat dissipation system, efficiently reduce the chip temperature, improve the performance and reliability of the entire SIP packaging system, and achieve efficient collaborative operation of the heat dissipation system. Therefore, the present invention can improve the accuracy and intelligence level of chip thermal management in SIP packaging.

[0165] like Figure 2 , which is a functional module diagram of an embedded microchannel heat dissipation system and a collaborative thermal management system for SIP packaging provided by one embodiment of the present invention.

[0166] The embedded microfluidic heat dissipation system and collaborative thermal management system 100 for SIP packages described in the present invention can be installed in electronic devices. Depending on the functionality implemented, the embedded microfluidic heat dissipation system and collaborative thermal management system 100 for SIP packages can include a heat dissipation channel division module 101, a prediction model training module 102, an optimal flow rate calculation module 103, and a priority chip cooling module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These are stored in the electronic device's memory.

[0167] The heat dissipation channel division module 101 is configured to receive a thermal management instruction and determine a thermal management system based on the thermal management instruction, wherein the thermal management system includes a working chipset, an embedded micro-channel, and a cooling pump, and the working chipset includes multiple working chips. The heat dissipation channel of each working chip in the working chipset is determined based on the embedded micro-channel to obtain a heat dissipation channel group, wherein each heat dissipation channel in the heat dissipation channel group is equipped with a micro-electromagnetic valve;

[0168] The prediction model training module 102 is used to collect current working data of each working chip in the working chipset to obtain a current working data group, wherein the current working data includes: current chip temperature, current chip power consumption and current chip load, obtain a training chip data set, and use the training chip data set to train a pre-built neural network model to obtain a cooling effect prediction model, wherein the training chip data in the training chip data set are all labeled;

[0169] The optimal flow rate calculation module 103 is configured to initialize a pre-constructed particle swarm based on a preset flow rate constraint range to obtain an initial particle swarm, wherein the initial particle swarm includes a plurality of initial particles, and the positions of the initial particles are the coolant flow rates of the cooling pump. The initial particle swarm is iterated based on the cooling effect prediction model and the current working data set to obtain the optimal coolant flow rate;

[0170] The priority chip cooling module 104 is used to use the current working data group to calculate the cooling priority of the working chipset in the thermal management system, obtain the chip cooling weight group, sort the working chipset based on the chip cooling weight group, obtain the priority chipset, and use the heat dissipation channel group and the optimal coolant flow rate to cool the priority chipset.

[0171] In detail, the modules in the embedded microchannel heat dissipation system and collaborative thermal management system 100 for SIP packaging in the embodiment of the present invention adopt the same method as above when in use. Figure 1 The embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging described in the present invention are the same technical means and can produce the same technical effects, so they will not be repeated here.

[0172] like Figure 3 , which is a structural diagram of an electronic device that implements an embedded microchannel heat dissipation system and a collaborative thermal management method for SIP packaging provided by an embodiment of the present invention.

[0173] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as an embedded microchannel heat dissipation system and a collaborative thermal management method program for SIP packaging.

[0174] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Furthermore, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the embedded microchannel heat dissipation system and the collaborative thermal management method program for SIP packaging, but can also be used to temporarily store data that has been output or is to be output.

[0175] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits. It executes programs or modules stored in the memory 11 (such as embedded microchannel cooling systems and collaborative thermal management method programs for SIP packages) and calls data stored in the memory 11 to perform various functions and process data of the electronic device 1.

[0176] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0177] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0178] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management system, thereby implementing functions such as charge management, discharge management, and power consumption management through the power management system. The power source may further include any components such as one or more DC or AC power sources, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may further include a variety of sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be described in detail here.

[0179] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0180] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0181] The embedded microchannel heat dissipation system and collaborative thermal management method program for SIP package stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve the following:

[0182] receiving a thermal management instruction, and determining a thermal management system based on the thermal management instruction, wherein the thermal management system includes a working chipset, an embedded micro-channel, and a cooling pump, and the working chipset includes a plurality of working chips;

[0183] Determine the heat dissipation channel of each working chip in the working chip group based on the embedded micro-channel to obtain a heat dissipation channel group, wherein the heat dissipation channels in the heat dissipation channel group are all installed with micro electromagnetic valves;

[0184] Collecting current working data of each working chip in the working chipset to obtain a current working data group, wherein the current working data includes: current chip temperature, current chip power consumption and current chip load;

[0185] Obtaining a training chip data set, and using the training chip data set to train a pre-built neural network model to obtain a cooling effect prediction model, wherein the training chip data in the training chip data set are all labeled;

[0186] Initializing a pre-constructed particle swarm based on a preset flow rate constraint range to obtain an initial particle swarm, wherein the initial particle swarm includes a plurality of initial particles, and positions of the initial particles are the coolant flow rate of the cooling pump;

[0187] Iterating the initial particle swarm according to the cooling effect prediction model and the current working data set to obtain an optimal coolant flow rate;

[0188] Using the current working data group, calculate the cooling priority of the working chipsets in the thermal management system to obtain a chip cooling weight group, and sort the working chipsets based on the chip cooling weight group to obtain a priority chipset;

[0189] The heat dissipation channel group and the optimal coolant flow rate are used to cool the priority chipset, completing the embedded microchannel heat dissipation system and collaborative thermal management for SIP packaging.

[0190] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0191] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0192] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0193] receiving a thermal management instruction, and determining a thermal management system based on the thermal management instruction, wherein the thermal management system includes a working chipset, an embedded micro-channel, and a cooling pump, and the working chipset includes a plurality of working chips;

[0194] Determine the heat dissipation channel of each working chip in the working chip group based on the embedded micro-channel to obtain a heat dissipation channel group, wherein the heat dissipation channels in the heat dissipation channel group are all installed with micro electromagnetic valves;

[0195] Collecting current working data of each working chip in the working chipset to obtain a current working data group, wherein the current working data includes: current chip temperature, current chip power consumption and current chip load;

[0196] Obtaining a training chip data set, and using the training chip data set to train a pre-built neural network model to obtain a cooling effect prediction model, wherein the training chip data in the training chip data set are all labeled;

[0197] Initializing a pre-constructed particle swarm based on a preset flow rate constraint range to obtain an initial particle swarm, wherein the initial particle swarm includes a plurality of initial particles, and positions of the initial particles are the coolant flow rate of the cooling pump;

[0198] Iterating the initial particle swarm according to the cooling effect prediction model and the current working data set to obtain an optimal coolant flow rate;

[0199] Using the current working data group, calculate the cooling priority of the working chipsets in the thermal management system to obtain a chip cooling weight group, and sort the working chipsets based on the chip cooling weight group to obtain a priority chipset;

[0200] The heat dissipation channel group and the optimal coolant flow rate are used to cool the priority chipset, completing the embedded microchannel heat dissipation system and collaborative thermal management for SIP packaging.

[0201] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only exemplary, and actual implementations may have other division methods.

[0202] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0203] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0204] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging, characterized in that: The method comprises: receiving a thermal management instruction, and determining a thermal management system based on the thermal management instruction, wherein the thermal management system includes a working chipset, an embedded micro-channel, and a cooling pump, and the working chipset includes a plurality of working chips; Determine the heat dissipation channel of each working chip in the working chip group based on the embedded micro-channel to obtain a heat dissipation channel group, wherein the heat dissipation channels in the heat dissipation channel group are all installed with micro electromagnetic valves; Collecting current working data of each working chip in the working chipset to obtain a current working data group, wherein the current working data includes: current chip temperature, current chip power consumption and current chip load; Obtaining a training chip data set, and using the training chip data set to train a pre-built neural network model to obtain a cooling effect prediction model, wherein the training chip data in the training chip data set are all labeled; Initializing a pre-constructed particle swarm based on a preset flow rate constraint range to obtain an initial particle swarm, wherein the initial particle swarm includes a plurality of initial particles, and positions of the initial particles are the coolant flow rate of the cooling pump; Iterating the initial particle swarm according to the cooling effect prediction model and the current working data set to obtain an optimal coolant flow rate; Using the current working data group, calculate the cooling priority of the working chipsets in the thermal management system to obtain a chip cooling weight group, and sort the working chipsets based on the chip cooling weight group to obtain a priority chipset; The heat dissipation channel group and the optimal coolant flow rate are used to cool the priority chipset, completing the embedded microchannel heat dissipation system and collaborative thermal management for SIP packaging.

2. The embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging according to claim 1, characterized in that: The obtaining of a training chip data set includes: Constructing a test thermal management system, wherein the test thermal management system includes: a test chipset, a test micro-channel and a test cooling pump; Extracting test chips from a test chip group of a test thermal management system in sequence, and collecting test data on the test chips to obtain a test chip data set, wherein the test chip data set includes a plurality of test chip data, and the test chip data includes: test chip temperature, test chip power consumption, test chip load, and test coolant flow rate; Each test chip data in the test chip data set is labeled to obtain a labeled chip data set; The labeled chip data sets corresponding to each test chip in the test chip set are merged to obtain a training chip data set.

3. The embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging according to claim 2, characterized in that: The step of labeling each test chip data in the test chip data set to obtain a labeled chip data set includes: Extracting test chip data in sequence from the test chip data set, and determining whether the test chip temperature in the test chip data is greater than a preset cooling temperature threshold; If the test chip temperature in the test chip data is greater than the cooling temperature threshold, the test chip data is marked with a preset invalid cooling tag to obtain marked chip data; If the test chip temperature in the test chip data is not greater than the cooling temperature threshold, determining the test time of the test chip data; Determine valid chip data in the test chip data set based on the test time and the preset effective cooling time, and judge whether the test chip temperature in the valid chip data is greater than the cooling temperature threshold; If the temperature of the test chip in the valid chip data is not greater than the cooling temperature threshold, the test chip data is marked with a preset valid cooling label to obtain marked chip data; If the test chip temperature in the valid chip data is greater than the cooling temperature threshold, the test chip data is marked with an invalid cooling tag to obtain marked chip data; The labeled chip data corresponding to each test chip data are summarized to obtain the labeled chip data set.

4. The embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging according to claim 3, characterized in that: The step of iterating the initial particle swarm according to the cooling effect prediction model and the current working data set to obtain the optimal coolant flow rate includes: Sequentially extracting initial particles from the initial particle group, determining initial positions of the initial particles, and calculating initial fitness of the initial particles according to the initial positions and a cooling effect prediction model; Summarizing the initial positions and initial fitnesses respectively to obtain an initial position set and an initial fitness set, and determining an optimal initial fitness in the initial fitness set; Based on the initial position set, the initial fitness set and the optimal initial fitness, the initial particle swarm is iterated until the change rate of the optimal initial fitness is no greater than the preset standard change rate, and the optimal initial fitness in the last iteration is recorded as the optimal population fitness; The optimal particle position corresponding to the optimal population fitness is determined, and the optimal coolant flow rate is determined based on the optimal particle position.

5. The embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging according to claim 4, characterized in that: The calculating the initial fitness of the initial particles according to the initial position and cooling effect prediction model includes: Extracting working chips in the working chip group in sequence, and determining working chip data corresponding to the working chips in the current chip data group, wherein the working chip data includes: working chip temperature, working chip power consumption, and working chip load; determining an initial coolant flow rate corresponding to the initial position; Inputting the working chip temperature, working chip power consumption, working chip load and initial coolant flow rate into the cooling effect prediction model to obtain a cooling effect value; Summarizing the cooling effect value corresponding to each working chip in the working chip group to obtain a cooling effect value group, and calculating the comprehensive effect value based on the cooling effect value group; Based on the initial coolant flow rate, performing a system balance analysis on the thermal management system to obtain a balance parameter group, wherein the balance parameter group includes: a cooling energy consumption value, a cooling pressure value, and a system wear value; Calculate the system balance value according to the balance parameter group; Based on the system balance value and the comprehensive effect value, the initial fitness of the initial particles is generated.

6. The embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging according to claim 5, characterized in that: Calculating the comprehensive effect value according to the cooling effect value group includes: Identify the maximum effect value in the cooling effect value group, and determine whether the maximum effect value is greater than a preset standard effect value; If the maximum effect value is not greater than the standard effect value, the comprehensive effect value is recorded as the preset zero value; If the maximum effect value is greater than the standard effect value, the cooling effect value group is averaged to obtain the comprehensive effect value.

7. The embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging according to claim 6, characterized in that: The system balance analysis of the thermal management system is performed based on the initial coolant flow rate to obtain a balance parameter group, including: Determining a system pressure drop according to a preset system resistance coefficient and the initial coolant flow rate, and recording the system pressure drop as a cooling pressure value; Obtaining a microchannel cross-sectional area of ​​the embedded microchannel, determining a cooling energy consumption power based on the microchannel cross-sectional area, an initial coolant flow rate, a system pressure drop, and a cooling pump, and recording the cooling energy consumption power as a cooling energy consumption value; Acquire a historical wear data set of the embedded microfluidic channel, determine a wear rate based on the historical wear data set, and record the wear rate as a system wear value, wherein the historical wear data set includes a plurality of historical wear data; The cooling pressure value, cooling energy consumption value and system wear value are summarized to obtain a balance parameter group.

8. The embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging according to claim 7, characterized in that: Calculating the system balance value according to the balance parameter group includes: Obtaining a baseline coolant flow rate of the embedded microchannel, and determining a baseline balance parameter group based on the baseline coolant flow rate, wherein the baseline balance parameter group includes: a baseline cooling energy consumption value, a baseline cooling pressure value, and a baseline system wear value; The system balance value is calculated based on the baseline balance parameter group and the balance parameter group, where the system balance value is expressed as: Wherein, T represents the system balance value, n represents the number of reference balance parameters in the reference balance parameter group or the number of balance parameters in the balance parameter group, represents the ith balance parameter in the balance parameter group, represents the i-th benchmark balance parameter in the benchmark balance parameter group, β i Represents the parameter weight coefficient of the preset i-th balance parameter.

9. The embedded microchannel heat dissipation system and collaborative thermal management method for SIP packaging according to claim 8, characterized in that: The method of calculating the cooling priority of the working chipsets in the thermal management system by using the current working data set to obtain a chip cooling weight set includes: Set the high temperature weight range and normal temperature weight range. The high temperature weight range is the numerical range of the chip cooling weight when the current chip temperature is less than the cooling temperature threshold, and the normal temperature weight range is the numerical range of the chip cooling weight when the current chip temperature is not less than the cooling temperature threshold. Extracting current working data in the current working data group in sequence; Construct temperature impact weights based on current chip power consumption and current chip load in current working data; Based on the temperature impact weight, high temperature weight range, normal temperature weight range, and the current chip temperature in the current working data, the chip cooling weight of the working chip corresponding to the current working data in the working chipset is calculated. The chip cooling weight is expressed as: Among them, q represents the chip cooling weight, k0 represents the minimum value in the normal temperature weight range, k1 represents the maximum value in the normal temperature weight range or the minimum value in the high temperature weight range, T represents the current chip temperature, T b represents the cooling temperature threshold, k2 represents the maximum value in the high temperature weight range, and q' represents the temperature influence weight; The chip cooling weights are aggregated to obtain a chip cooling weight group.

10. An embedded microchannel heat dissipation system and collaborative thermal management system for SIP packaging, characterized in that: The system comprises: a heat dissipation channel division module, configured to receive a thermal management instruction and determine a thermal management system based on the thermal management instruction, wherein the thermal management system includes a working chipset, an embedded micro-channel, and a cooling pump, and the working chipset includes multiple working chips; the heat dissipation channel of each working chip in the working chipset is determined based on the embedded micro-channel to obtain a heat dissipation channel group, wherein each heat dissipation channel in the heat dissipation channel group is equipped with a micro-electromagnetic valve; A prediction model training module is used to collect current working data of each working chip in the working chipset to obtain a current working data group, wherein the current working data includes: current chip temperature, current chip power consumption and current chip load, obtain a training chip data set, and use the training chip data set to train a pre-built neural network model to obtain a cooling effect prediction model, wherein the training chip data in the training chip data set are all labeled; An optimal flow rate calculation module is used to initialize a pre-constructed particle swarm based on a preset flow rate constraint range to obtain an initial particle swarm, wherein the initial particle swarm includes multiple initial particles, and the positions of the initial particles are the coolant flow rate of the cooling pump. The initial particle swarm is iterated based on the cooling effect prediction model and the current working data set to obtain the optimal coolant flow rate; The priority chip cooling module is used to use the current working data group to calculate the cooling priority of the working chipset in the thermal management system, obtain the chip cooling weight group, sort the working chipset based on the chip cooling weight group, obtain the priority chipset, and use the heat dissipation channel group and the optimal coolant flow rate to cool the priority chipset.

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