918 results about "Memory module" patented technology

The invention discloses a knowledge graph reasoning method based on dynamic rule perception memory. The method aims at solving the problems that an existing neural combination rule learning method is insufficient in expression ability and prone to splitting global semantics and local relation modes. The method comprises the following steps: introducing a lightweight dynamic relationship memory module, and executing self-attention on all relationships in a knowledge graph to capture global semantics; meanwhile, local relation interaction features are extracted through convolution, semantic fusion is carried out through a Transform encoder with multi-head attention and relative position coding, and unified and parallel-computing high-quality combination representation is constructed for each pair of relations in parallel. And meanwhile, a closed high-quality reasoning path is generated in combination with bidirectional breadth-first search. Through global semantic and local interactive collaborative modeling, the extendibility is ensured, the understanding of global semantics is enhanced, and the reasoning accuracy is improved. Global and local relation dependence is fused, an interpretable reasoning path is generated, and reasoning accuracy, expandability and robustness are improved.

The invention relates to the technical field of storage control, in particular to a memory control method, which comprises the following steps: acquiring a target logic address in response to a reading request from a host system; determining a data access mode associated with the target logic address in real time based on the historical access information, wherein the data access mode at least comprises a continuous mode, a random mode and a hotspot mode; according to the determined data access mode, triggering a data prefetching strategy and a flash translation layer query strategy matched with the access mode; obtaining target data from a memory module based on a data prefetching strategy and a flash translation layer query strategy; and scheduling the cache resources and the input / output bandwidth according to a predetermined resource management strategy, and returning the target data to the host system. The prefetching accuracy and the cache utilization rate are improved.

The invention discloses a consumer feedback analysis method based on a multi-agent model, and the method comprises the steps: firstly sampling and generating consumer multi-dimensional feature data, and expanding the data into a semantic portrait through a large model; therefore, an intelligent agent containing a memory module is constructed, and part of the intelligent agent is set as an opinion leader. Commodity attributes, propaganda and competitive products are input into each agent, and initial evaluation is independently output; and then multiple rounds of viewpoint interaction are carried out in the same social evaluation area, the opinion leader has a higher sampled weight, and memory and comments are updated. And after simulation is finished, updating evaluation is generated again and compared with the initial evaluation, attitude changes are analyzed, all data are summarized, and a visual comprehensive report is output. According to the method, accurate simulation and feedback analysis of consumer behaviors are realized by combining a large language model and a multi-agent simulation test technology, and the defects of a traditional method in the aspects of consumer portrait dynamics, agent adaptability, interaction complexity, data dependence and the like are overcome.

The invention discloses an intelligent data center energy scheduling optimization method based on machine learning. The method comprises the following steps: acquiring data to form a multivariable time sequence; the improved PatchTST model calls a multi-level memory interaction structure to establish short-term memory and long-term memory, and an attention constraint energy consumption memory module is introduced to output prediction features; inputting the prediction features into a decoding layer of the improved PatchTST model; constructing an energy scheduling multi-objective optimization problem, and generating an equipment control instruction set by adopting an NSGA-III algorithm; collecting an actual execution result to form feedback data; performing deviation evaluation and strategy updating on feedback data through an energy self-feedback correction process; and judging whether the energy consumption prediction error and the energy scheduling deviation meet a preset termination condition or not. The method is suitable for intelligent energy scheduling and operation efficiency optimization in multi-energy coupling scenes such as an intelligent data center.

The invention discloses a real-time data receiving and transmitting system and method based on a kernel bypass, and the method comprises the steps: processing the received data when the data receiving and transmitting system receives the data, and achieving the high-level data with complete semantics; updating a write pointer through atomic operation, and writing high-level data into a lockless annular queue in the shared memory module; the upper-layer application module reads the lock-free annular queue in the shared memory; when the data receiving and transmitting system transmits data, upper-layer data to be transmitted is transmitted to the shared memory module, and a write pointer is updated; the upper layer data in the shared memory is read in a polling or interruption mode, a read pointer is updated, the upper layer semantic data is segmented according to the format, a UDP data packet is obtained, the UDP data packet is written into a network card in the polling or interruption mode, and data sending is achieved. Through the data receiving and transmitting system disclosed by the invention, microsecond-level ultra-low delay of data certainty is realized, and efficient decoupling and stable throughput are realized.

A method for processing faulty memory module and apparatus, and an electronic device and a non-transitory readable storage medium are provided. The method for processing faulty memory module includes: acquiring a first power-down command, the first power-down command being used for instructing a baseboard management controller of a memory resource pool to perform power-down processing on a faulty memory module in the memory resource pool; and in response to the first power-down command, when a first memory module in the memory resource pool is in a fault state and is allowed to be powered down, sending a second power-down command to a target memory expander controller to which the first memory module belongs, the second power-down command being used for instructing the target memory expander controller to perform power-down processing on the first memory module.

The invention provides a body navigation method, device and equipment based on brain inspiration structured spatial memory, and relates to the technical field of navigation.The method comprises the steps that a work memory module is used for determining a structured spatial memory module for searching a navigation instruction according to the complexity of the received navigation instruction, and the structured spatial memory module is used for searching the navigation instruction; the structured spatial memory module comprises at least one of a landmark memory module and a cognitive map module; responding to the navigation instruction, and performing hierarchical retrieval by using a structured spatial memory module of the navigation instruction to obtain a candidate coordinate set of the target; the candidate coordinate set comprises a plurality of candidate coordinates; determining an exploration sequence based on the confidence score of each candidate coordinate and the distance between each candidate coordinate and the coordinate of the current position of the body agent; the exploration sequence represents the exploration sequence of the candidate coordinates; and controlling the body agent to execute navigation operation based on the exploration sequence. According to the method, the robust space cognition and self-adaptive navigation capability of the intelligent agent with the body in a complex environment is realized.

The invention discloses an enterprise-level agent system and a data processing method. The system mainly comprises a sensing module, a memory module, a planning module, a Routine agent planning engine, an arrangement engine and an execution module. According to the enterprise-level agent system provided by the embodiment of the invention, short-term memory, long-term memory and enterprise-level memory are combined through a complete closed-loop architecture of the sensing module, the memory module, the planning module and the execution module, and precipitation of personalized and organization-level knowledge is supported; and meanwhile, in combination with a standard business process template provided for an enterprise by a Routine agent planning engine and splitting of a task process by an orchestration engine, role agents are allocated for each sub-task, so that coordination and cross-system orchestration of complex tasks are realized, the construction threshold of intelligent applications is reduced, and the business efficiency is improved.

A retention mechanism is provided to couple a compression attached memory module (CAMM) to a printed circuit board (PCB). The retention mechanism includes a retention bracket and a retention spring. The retention bracket is soldered by a solder flange to the PCB and retains a CAMM connector within the retention bracket. The retention spring retains the CAMM and compresses the CAMM onto the CAMM connector and the PCB.

The invention provides a dual-runtime engine collaboration method and system for an industrial edge computing terminal. Relates to the technical field of industrial edge computing. The method is applied to a system comprising a real-time runtime engine, a non-real-time runtime engine, a shared memory module and a resource monitoring module, and comprises the following steps: executing a real-time task and / or a non-real-time task, and performing data interaction between the real-time runtime engine and the non-real-time runtime engine through the shared memory module, periodically acquiring a resource use state of a hardware platform through a resource monitoring module, and dynamically adjusting available resources allocated to an engine during non-real-time operation according to the resource use state; and when the idle time window exists in the scheduling period of the real-time runtime engine, executing the non-real-time task in the idle time window through the non-real-time runtime engine. According to the invention, collaborative optimization of the system on the aspects of time certainty, flexible resource allocation and low-latency data exchange can be realized.

The embodiment of the invention provides a data management method and device, electronic equipment and a storage medium. The method comprises the following steps: receiving a data access request for target data; the data access request carries a to-be-accessed logic address corresponding to target data; determining a target logic address section to which the to-be-accessed logic address belongs; determining a target physical space block corresponding to the target logic address section in a preset mapping relation; executing an access operation of the target data in the target physical space block according to the to-be-accessed logic address; the preset mapping relationship comprises a one-to-one correspondence relationship between each logical address section and each physical space block of the memory module. Visibly, in the technical scheme, the data volume in the preset mapping relationship can be greatly reduced, so that the storage space occupied by storing the mapping relationship is reduced, the memory space overhead is saved, the query speed can be improved, and the data access delay caused by searching the mapping relationship is reduced.

Described are memory modules that include address-buffer components and data-buffer components that together support wide- and narrow-data modes. The address-buffer component manages communication between a memory controller and two sets of memory components. In the wide-data mode, the address-buffer enables memory components in each set and instructs the data-buffer components to communicate full-width read and write data by combining data from or to from both sets for each memory access. In the narrow-data mode, the address-buffer enables memory components in just one of the two sets and instructs the data-buffer components to half-width read and write data with one set per memory access.

The invention discloses a multi-agent-based cross-station intelligent inspection and operation and maintenance method and system, and the system comprises an inspection expert agent, a tool use agent, a code generation agent, an overhaul expert agent and a memory module. Calling a tool using agent to obtain corresponding power station information generation suggestions, and issuing corresponding tasks after user confirmation; a code generation agent receives the task, a tool use agent is called to obtain a corresponding power station model version and hardware equipment information generation script, and script parameters are dynamically adjusted; and the maintenance expert agent obtains the script operation detection model, calls the tool use agent to obtain corresponding power station monitoring information, detects the monitoring information by using the detection model, stores the detection result to the short-term memory module, and gives a maintenance suggestion and adjusts a detection strategy by combining the detection result with the maintenance knowledge information in the long-term memory module. According to the invention, efficient, intelligent and sustainable automatic task management and execution are realized.

Systems, apparatus, articles of manufacture, and methods to improve cooling of dual in-line memory modules are disclosed. An example apparatus includes a heat pipe to extend between first and second dual in-line memory modules (DIMMs). A first end of the heat pipe extends beyond a first end of the first and second DIMMs. A second end of the heat pipe extends beyond a second end of the first and second DIMMs. The example apparatus further includes an array of fins thermally coupled to the first end of the heat pipe.

The invention relates to the technical field of data storage, and discloses a method for testing the signal quality of an IO interface of a memory module, and the method comprises the steps: verifying the signal integrity of the I / O interface in the memory module based on an eye diagram analysis method; after the signal is verified to be complete, the morphological characteristics of the input eye pattern and the output eye pattern are quantitatively compared, whether morphological characteristic changes occur in the signal transmission process or not is judged, and the morphological characteristic changes at least comprise logic level flipping errors, time sequence recognition errors and signal quality degradation; if the morphological characteristics of the input eye pattern are consistent with the morphological characteristics of the output eye pattern, the test is successful; and if the morphological characteristics of the input eye pattern are inconsistent with the morphological characteristics of the output eye pattern, determining that the test fails. Visual and quantitative verification of signal integrity is realized through eye pattern analysis, multi-dimensional indexes such as voltage, time sequence and noise are covered, and the signal transmission quality is reflected more comprehensively compared with a single parameter test.

A marking element for tree trunks, with a base plate that has an striking surface and a cutting surface facing away from the striking surface. A cutting edge protrudes from the cutting surface and an electronic information carrier is attached to the base plate, which has a memory module and an antenna that is electrically connected to the memory module. The antenna has an antenna section which protrudes laterally beyond the striking surface and beyond the cutting surface.

The invention belongs to the technical field of verification of an electric digital integrated module, and provides a verification method and system for a reusable storage module based on a UVM (Universal Verification Module), and the method comprises the steps: simulating a multi-host concurrent access scene through a UVM component, and generating and synchronously outputting an excitation signal to a to-be-verified storage module and a reference model; acquiring a reference operation result and an actual operation result generated by the to-be-tested storage module according to the excitation signal; according to the design function points of the to-be-verified storage module, the actual operation result and the reference operation result are compared and verified, and a verification result is obtained; if the verification result is that at least one design function point in the actual operation result and the reference operation result is inconsistent, it is judged that the function of the to-be-verified storage module is abnormal in the multi-host concurrent access scene, and key signal waveform data and key log data of the to-be-verified storage module are recorded and reported. According to the scheme, the comprehensiveness and the accuracy of verification are improved, and the function reliability and the verification efficiency of the storage module in a complex concurrent environment are improved.

A semiconductor system may include a command address bus, a data bus, a management bus, a memory module including multiple memories and coupled to the command address bus, data bus and management bus, a memory controller configured to control, through the command address bus and the data bus, the memory module to perform a write operation and a read operation and a baseboard management controller configured to perform, through the management bus, a management operation of managing the multiple memories.

The invention provides a memory management method and a storage device. The method comprises the following steps: obtaining a plurality of evaluation parameters corresponding to a first entity unit; determining a priority state of the first entity unit according to the evaluation parameters; and determining a selected entity unit for performing a data migration operation on the memory module according to the first priority state. Therefore, the operation efficiency and / or stability of the storage device can be improved.

The invention provides a maze robot brain-like cognitive system based on an emotion and memory mechanism, and the system comprises a perception and emotion generation module which obtains the environment state of a robot, and calculates the reflective fear value and cognitive fear value of the robot; the memory module is used for recording short-time memory, working memory and long-time memory of the robot in combination with an anti-thought memory mechanism; the behavior decision-making module is used for comparing the evaluation function based on the cognitive fear value with the energy margin to obtain a behavior decision-making result; the learning module is used for exploring and learning a target node and an energy node to obtain an updated target guide table and an updated energy guide table; and the action selection module is used for updating the target guide table and the energy guide table according to the behavior decision result, and outputting the next action. According to the invention, the survival rate of the robot in the environment learning process can be improved, and long-time learning is realized; and the robot can be prevented from repeatedly exploring in an invalid area, the learning efficiency is improved, the energy consumption is reduced, and self-survival is ensured.

A computing device includes at least one processor, one or more non-transitory computer-readable storage media, a system for fine-tuning a large language model under low-bit weight-activation quantization. The computing device further comprises a graphics processing unit (GPU), a neural processing unit (NPU), or a tensor processing unit (TPU). The hardware interface module of the system is configured to load a low-bit model representation from the memory module and transmit the model representation to the GPU, NPU, or TPU for inference execution.

Memory devices on a memory module may include two (or more) interfaces that access non-overlapping sets of memory arrays (i.e., two pseudo-channels). The data bus directionality of each of the two pseudo-channels may be specified or required to always be in the same direction (i.e., both read, or both write). When the data bus directionalities are tied in this manner, the data buffer devices of the module may interleave / deinterleave (i.e., time-multiplex) data for communication with a host between the two pseudo-channel data busses. The data bus directionality of each of the two pseudo-channels may be independent of the directionality of the other pseudo-channel (i.e., either can read or write without regard to the data bus directionality of the other). When the data bus directionalities are independent in this manner, the data buffer devices may interleave / deinterleave data for communication with a host within each of the two pseudo-channel data busses.

Provided are systems, methods, and apparatuses for systems and methods of programming processes in processing in memory (PIM) systems. In one or more examples, the systems, devices, and methods include enabling, via a processor, a PIM manager for processing of PIM instructions, the PIM manager being located on a base die of a stacked memory module; enabling, via the PIM manager, a plurality of processing units of one or more memory dies of the stacked memory module for processing; compiling source code comprising the PIM instructions into machine code; loading, via PIM manager, the machine code into an allocation of memory of the stacked memory module; and executing, via the plurality of processing units, the PIM instructions based on loading the machine code into the memory of the stacked memory module.

The invention provides a memory control method and a storage device.The method is applied to the storage device comprising a memory module, the memory module comprises a plurality of blocks, and when a garbage collection mechanism is triggered, at least one garbage collection source block is determined; acquiring distribution information of effective data of the garbage collection source block; based on the distribution information, calculating at least one quantization parameter used for representing data distribution characteristics; based on a preset decision model, obtaining a decision value according to the quantization parameter; according to the decision value and a preset datum line, a reading mode of this time of garbage collection is determined, and the reading mode comprises a first mode and a second mode; and reading valid data in the garbage collection source block based on the determined reading mode, and writing the valid data into the target block. And by dynamically matching the data distribution characteristics with the optimal reading mode, the garbage collection efficiency is remarkably improved, and the overall performance of the storage device is improved.

The invention discloses a video target tracking system based on historical information dynamic propagation and future frame prediction collaborative optimization, which belongs to the field of visual target tracking and comprises a dynamic attention token module, a multi-constraint future prediction module, a real-time bidirectional calibration module, a tracking decision module and a scene adaptive memory module. The dynamic attention token module is used for receiving continuous video frames and generating a multi-scale token sequence dynamically adjusted along with target states, and the target states comprise a shielding area, a movement speed and an appearance scale; the target tracking robustness problem in a complex scene is solved by dynamically fusing the historical trajectory features of the target and future state prediction, and the method can be applied to the fields of intelligent monitoring, automatic driving visual perception, unmanned aerial vehicle inspection, robot visual navigation and the like. A bidirectional optimization mechanism based on historical token propagation and future frame prediction breaks through the limitations of insufficient utilization of time sequence information and insufficient dynamic adaptability in the prior art.

The invention discloses an SRAM (Static Random Access Memory) verification method, system and equipment, relates to the technical field of integrated circuits, and is used for solving the problems of low code reuse rate, long verification period and difficulty in defect positioning in the existing SRAM verification method. The method comprises the following steps: setting a unified virtual shell program module for a to-be-verified SRAM memory module, and packaging key attribute information of the to-be-verified SRAM memory module through macro definition parameters; distributing a serial number for the SRAM type of each SRAM memory module to be verified, and establishing a mapping relation between the serial number and the interface module; calling a corresponding excitation signal from an excitation signal library according to the serial number, and determining a corresponding target interface module through a mapping relation; and connecting a virtual shell program module of the SRAM module to be verified through the target interface module, sending an excitation signal, and triggering function verification. According to the technical scheme provided by the invention, high efficiency, compatibility and expansibility of verification can be realized, and the accuracy of a verification result is improved.

A memory package may include multiple memory devices and an additional die in some examples. The memory package may be included on a memory module. The memory module may include multiple memory packages. The additional die may include components that reduce or eliminate a number of components on the memory module. In some embodiments, the additional die includes redundant storage for storing data associated with defective memory cells in the memory arrays on the multiple memory devices. The redundant storage can supplement redundant memory on the multiple memory devices or replace the redundant memory on the multiple memory devices.

The invention discloses an intelligent agent-based interactive simulation system and an interactive simulation method, and belongs to the field of artificial intelligence. The system comprises a control module serving as a core control center, a world module providing a simulation environment for multi-agent interactive simulation, a memory module serving as an agent memory library and a tool module integrating various auxiliary tools. When the system is used for performing a topic discussion simulation task, the modules cooperate to perform interactive simulation, so that each agent generates comments. The invention aims to improve the expandability of the social simulation system, so that the social simulation system can process large-scale individual interaction; the accuracy of a simulation result is improved, and the emotion and psychological states of individuals are considered; and a complex organization structure in a human society is effectively simulated, so that the authenticity of a simulation effect is enhanced.

The invention discloses an epileptic seizure video detection method based on a memory enhanced double-branch auto-encoder. The method comprises the following steps: firstly, preparing and preprocessing data; secondly, constructing an ME-DBAE model which comprises a query generation module, a multi-head memory module and a double-branch decoder module; training the model by using training set data, finally inputting a to-be-detected video into the trained model to obtain an epileptic seizure score, and judging whether the video belongs to an epileptic seizure sample or not through a threshold value. By designing the multi-head memory module, diversified normal motion prototypes are stored in parallel, and the limitation that a single memory structure is difficult to adapt to complex normal actions is broken through; and by constructing a double-branch decoder architecture, double tasks of input reconstruction and future frame prediction are cooperatively realized, and meanwhile, space structure and time trend characteristics of normal motion are captured, so that epileptic seizure actions and normal actions are effectively distinguished.

The invention provides a data generation system, method and device, electronic equipment and a storage medium, relates to the field of artificial intelligence, and is mainly technically characterized by comprising a planning module, a control module and a memory module, wherein the planning module is used for generating a target execution plan comprising at least one subtask according to a user intention, and sending the target execution plan to the control module; the control module is used for calling a target tool in the tool library to execute at least one subtask in the received target execution plan; the execution result of the current subtask in execution is evaluated to generate an evaluation result, and the execution strategy of the current subtask is adjusted according to the evaluation result until the subtask with the requirement for adjusting the execution strategy is adjusted; the memory module is used for storing the target execution plan and information generated in the execution process of at least one subtask; and the control module is also used for obtaining and generating a query result corresponding to the user intention from the memory module.