45 results about "Hardware specification" patented technology

A method or system for implementing a machine learning pipeline on a chipset comprising a plurality of hardware compute elements. The system accesses a hardware-agnostic functional description of the machine learning pipeline, wherein the description specifies a plurality of functional modules, including at least one machine learning model. Hardware specifications of the chipset are accessed to identify the available hardware compute elements. Based on the hardware specifications, the functional modules are synthesized into a plurality of interconnected executable components configured to execute on at least two different hardware compute elements. An implementation package is generated, comprising the executable components and metadata describing interconnections between them. The implementation package is then deployed to the chipset, where the executable components are executed by the identified hardware compute elements.

The invention provides a method and system for dispatching heterogeneous GPUs through cross-cluster management software, and relates to the technical field of GPU resource dispatching.The method comprises the steps that physical topology data, hardware specification data and historical task records of GPU nodes are obtained through the cross-cluster management software, an association degree matrix between GPU units is established, and a topology grouping set is generated; calculating a communication efficiency index based on topology detection analysis, constructing a topological graph model, and determining an optimal communication path of a multi-card task; generating a perception scheduling strategy in combination with a multi-objective optimization algorithm, and selecting an optimal GPU combination from the grouping set; in the task execution process, a scheduling strategy is dynamically adjusted according to the real-time cluster state and task efficiency data, continuous adaptation of a physical topological structure and task requirements is kept, and finally efficient collaborative scheduling of cross-cluster heterogeneous GPU resources is achieved. According to the method, the scheduling efficiency of heterogeneous GPU resources in a distributed computing scene and the overall performance of the system are improved.

According to the chip system, the data transmission method and the related equipment provided by the embodiment of the invention, the chip system comprises IO core particles and a plurality of calculation core particles, each calculation core particle is configured with a corresponding core particle center, and each calculation core particle is connected with at least two physical interconnection ports in the IO core particles through the core particle center; the core grain center comprises a splitting unit and a merging unit, and the splitting unit is used for distributing data streams sent by the computing core grains to at least one physical interconnection port according to service types; the merging unit is used for recombining the data stream received from the physical interconnection port or directionally forwarding the data stream to the computing core grain, and the data throughput rate and the overall transmission efficiency in a large-scale parallel computing scene are remarkably improved on the premise that the hardware specification of the external IO core grain does not need to be modified.

The invention discloses a convolutional neural network performance prediction method and system based on hardware interaction features, and relates to the technical field of artificial intelligence and computer system performance optimization, and the method comprises the steps: obtaining hierarchical features of a convolutional neural network model; based on the hierarchical features, combined analysis is carried out in combination with the hardware specification of the target hardware platform, and interaction features are obtained through calculation; a Transform model with a bias matrix is adopted for prediction, interaction features are processed by a hardware perception attention bias module to generate a hardware perception bias matrix, and the hardware perception bias matrix is input to a Transform encoder; and after the hierarchical features are processed by an input encoder, a Transform encoder and a linear decoding layer in sequence, the running time of the convolutional neural network model is predicted. According to the method, the reasoning training time is predicted by statically analyzing the architecture of the convolutional neural network model and the specification of the target hardware.

This disclosure is directed to methods and systems that establish a secure data channel between a host and a disaggregated hardware device (“DHD”) of a data center. The system comprises an initiator host that runs objects, such as virtual machines and containers. The host includes an initiator smart network interface card (“SNIC”). The initiator SNIC includes a virtual device, a trust platform module (“TPM”) and a security engine. The system also comprises a target host equipped with a DHD and a target SNIC. The target SNIC includes a TPM and a security engine. The TPM and the security engine of the initiator SNIC and the TPM and the security engine of the target SNIC establish a secure data channel between an object running on the host and the DHD.

The invention provides a communication method and a communication device, and the method comprises the steps that a first device can obtain one or more first phase differences between downlink measurement channels of a first TRP and a second TRP through receiving a first channel state information report reported by a terminal; and then determining a second phase difference between the uplink measurement channels of the first TRP and the second TRP, and determining a phase calibration coefficient between the first TRP and the second TRP based on the first phase difference and the second phase difference, thereby adjusting the sending or receiving phase of the TRP based on the phase calibration coefficient when the TRP sends a downlink reference signal or receives an uplink reference signal next time. Realization of reciprocity correction between TRPs is facilitated under the condition that the TRP hardware specification is not limited, and improvement of data transmission performance is facilitated. Moreover, the first channel state information report reported by the terminal only needs to carry phase-related information (such as the first phase difference), so that the feedback overhead can be reduced.

Broadly speaking, the present techniques generally relate to a method for training a machine learning, ML, model to perform semantic image segmentation, and to a computer-implemented method and apparatus for performing semantic image segmentation using a trained machine learning, ML, model. The training method enables a semantic image segmentation ML model that is able to make predictions faster, without significant loss in accuracy. The training method also enables the ML model to be implemented on apparatus with different hardware specifications, i.e. different computational power and memory, for example.

The invention provides a flash memory data packet writing method and device, and relates to the field of flash memory data packet writing and garbage collection optimization. The method comprises the steps of obtaining file features, user behavior features and system metadata features of to-be-written data, and predicting a life cycle frequency band of the data based on multi-dimensional features; according to the predicted life cycle frequency band, aggregating data with similar failure characteristics into data groups, and matching the data groups to corresponding physical storage blocks; writing the data in the data group into the same physical storage block adaptive to the hardware specification so as to realize high synchronization of data failure time; and when the valid data volume in the data group is lower than a preset threshold value, triggering a garbage collection mechanism, and directly executing a whole block erasing operation on the physical storage block to avoid valid data migration. According to the method provided by the embodiment of the invention, write amplification and system delay can be effectively reduced, the service life of the flash memory storage system is prolonged, and the storage performance and the resource utilization rate are improved.

The invention provides a communication method and device for a GPU cluster, electronic equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical field of distributed computing, GPU cluster communication optimization and the like. According to the implementation scheme, the method comprises the steps of obtaining network performance data among a plurality of GPU nodes in a GPU cluster and hardware specification data of each GPU node in the plurality of GPU nodes; generating a hybrid logic communication topology for the plurality of GPU nodes based on the network performance data and the hardware specification data, the hybrid logic communication topology being a combination of at least two of a tree topology, a ring topology and a bus topology; determining a communication protocol adopted by a communication link in the hybrid logic communication topology based on the network performance data; and communicating among the plurality of GPU nodes according to the generated hybrid logic communication topology and communication protocol.

The invention discloses an efficient linkage method between equipment based on an open source gap, and relates to the field of industrial Internet of Things. The method comprises the following steps: determining a control instruction and a state reading instruction of a mechanism through a reference technical manual and a hardware specification; hardware drive adaptation of the open-source swan-monk system is completed, and system source codes are subjected to cross compiling and deployed to hardware development equipment; writing a bottom layer source code for reading and controlling the state of the mechanism; the bottom layer source code is packaged into an NAPI interface, so that an upper layer interface can conveniently call; developing an operation interface suitable for the swan monk system, calling an NAPI interface on an upper-layer interface, compiling corresponding task logic, and burning the task logic to two hardware devices respectively; codes for communication between the equipment in the gap are written by using the distributed soft bus characteristics of the gap. According to the method, efficient linkage between the devices can be achieved, the method has important practical value for achieving intelligentization, and development and innovation of the domestic Internet of Things technology are promoted.

The invention discloses a screen brightness adjusting method, the adjusting method is applied to a screen of an LED layer, the LED layer is divided into a plurality of LED blocks, and the adjusting method comprises the following steps: S1, obtaining hardware configuration parameters of the screen; s2, based on the maximum power value and the total number of the blocks, calculating the theoretical maximum number of the blocks which can be driven by the LED layer under the constraint of the maximum power; s3, current ambient light parameters are collected in real time; s4, dynamically generating a target window value through a pre-trained brightness compensation model according to the ambient light parameters and the theoretical maximum block number; and S5, generating a corresponding driving control parameter according to the target window value, and controlling a screen to display. By combining hardware power constraint modeling, ambient light intelligent perception, display content self-adaptive identification and closed-loop power consumption control, the problems that in the prior art, complex ambient light adaptability is poor, screen hardware specification compatibility is insufficient, and over-drive and non-over-drive screen management is not unified are effectively solved.

Mechanisms to transform a multimodal hardware specification document into register-transfer level (RTL) code by configuring a large language model into multiple agents to transform the hardware specification document into a structured implementation plan comprising a sequence of hardware functions, configuring the large language model to apply progressive coding and prompt optimization to sequentially transform the hardware functions into low-level program code through a plurality of progressively lower-level code generation stages, and transforming the low-level program code of the hardware functions into the RTL code through a code optimizer and high-level synthesis tool.

A digital engineering ecosystem may use artificial intelligence to generate a virtual hardware interface to emulate a hardware interface described in one or more hardware specifications. The virtual hardware interface may receive executable code configured to execute on the hardware interface. The virtual hardware interface may execute the received executable code, and, after being successfully executed, the execution of the executable code may be logged.

Relatively simple user input is used to build an entire application. Input data defines a set of specifications for an automated machine. The set of specifications includes one or more of hardware specifications, operating systems, software frameworks, runtime environments, network connectivity, and environmental considerations. The input data is analyzed to retrieve relevant information from a large corpus including a plurality of libraries. One or more prompts are generated based on the relevant information, and the one or more prompts are used to generate programmable logic controller (PLC) code including a sequence of operations for controlling the automated machine.

Embodiments of the present application provide a hardware specification test tool, a method and related equipment, wherein the hardware specification test tool comprises: a plurality of special test modules; one special test module tests one hardware specification of an acceleration computing chip; different special test modules are aimed at different hardware specifications to cover all hardware specifications of the acceleration computing chip; when testing the hardware specification, one or more special test modules are dispatched, and the dispatched special test module tests the hardware specification in at least one dimension; wherein one dimension corresponds to a quantifiable performance item of the hardware specification in a specific test direction, and multiple dimensions cooperatively cover the overall performance characteristics of the hardware specification. The technical scheme provided by the embodiments of the present application can perform integrated and complete hardware specification testing on the acceleration computing chip, and improve the test comprehensiveness of the hardware specification test tool.

The invention relates to a method for generating a task-specific adapted topology for hardware based on quantum electrodynamics (cQED), characterized in that it comprises the iterative steps of: - determining an error committed by a circuit initially obtained by transpilation of an M agnostic qubit generated using a client task; - comparing the transpiled circuit error with an error threshold corresponding to the circuit of M agnostic qubits; - if the transpiled circuit error is greater than the error threshold, modifying some of the hardware specifications to be processed in the transpilation step; - if the transpiled circuit error is less than the error threshold, supplying the transpiled circuit as a task-specific adapted topology to a chip design and nanofabrication unit.

The chip system, the data transmission method and the related equipment provided in the embodiments of the present application, the chip system comprises: an IO chip and a plurality of computing chips, each computing chip is configured with a corresponding chip hub, and each computing chip is connected with at least two physical interconnection ports in the IO chip through the chip hub; the chip hub comprises a splitting unit and a merging unit, the splitting unit is used for distributing the data stream sent by the computing chip to at least one physical interconnection port according to the service type; the merging unit is used for recombining or directionally forwarding the data stream received from the physical interconnection port to the computing chip, without modifying the external IO chip hardware specification, the data throughput rate and the overall transmission efficiency in the large-scale parallel computing scene are significantly improved.

The application relates to an application binary file standardization processing method and system, and the method comprises the following steps: establishing an application binary file standard library according to a system specification system file; obtaining a list of application binary files to be processed, identifying an application category according to a file attribute, and extracting a corresponding checking strategy; performing standard checking on the file according to the checking strategy; determining, by a user, whether to perform standardization processing on a non-standard list; packaging the processed application binary file to obtain a deb application package; recording process log information and test results, and archiving the deb application package. According to the application, a corresponding standard library is established according to a software and hardware specification system file, checking rules are automatically identified and extracted, standardization checking and processing of the binary file are completed, and a standard deb package is automatically generated. According to the application, a binary file standard library can be established according to core specifications which independently affect the compatibility of an operating system, the checking efficiency is improved, and the standardization and compatibility of the application on the operating system are ensured.

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and / or the like for receiving pre-imaging data for a plurality of pre-imaged computing devices; generating, using the pre-imaging data, a plurality of data objects that respectively correspond to the plurality of pre-imaged computing devices, wherein a first data object of the plurality of data objects comprises a data construct that describes a first computing device of the plurality of pre-imaged computing devices; receiving a computing device provisioning request comprising one or more hardware specifications and application criteria; determining a computing device from the plurality of pre-imaged computing devices based at least in part on the one or more hardware specifications and the plurality of data objects; generating a custom build instruction identifying the application criteria; and transmitting the custom build instruction to the computing device.

The invention relates to a multi-equipment cooperative control method and device, an air conditioning system and a storage medium. The method comprises the following steps: identifying a first device and a second device which have the same device model and different device functions; only the first equipment has a target function; after a cooperative control instruction for the target function is received, a control action executed by the first equipment after the target function is started is determined; and controlling the second equipment to execute the same control action as the first equipment according to the control action executed by the first equipment after the target function is started. Due to the fact that the hardware specifications of the devices of the same device model are the same, under the condition that the first device has the target function and the second device does not have the target function, if the multiple devices need to cooperatively execute the target function, the second device can be controlled to execute the same control action as the first device; and the target function of the first equipment is multiplexed into the second equipment, so that the purpose that the first equipment and the second equipment uniformly execute the same function is achieved.

An electronic device and a control method of the electronic device are provided. The electronic device includes first device information based on a hardware specification of the electronic device, second device information on a hardware specification of a first external device, first model information on one or more neural network models included in the first external device, and identifying whether each of the one or more neural network models included in the first external device is suitable for hardware of the electronic device with respect to second model information of the one or more neural network models included in the electronic device, and whether each of the one or more neural network models identified as suitable for hardware of the electronic device is suitable for replacing a neural network included in the electronic device.

The application provides a method and system for scheduling heterogeneous GPUs by cross-cluster management software, and relates to the technical field of GPU resource scheduling, wherein the method comprises obtaining physical topology data, hardware specification data and historical task records of GPU nodes through cross-cluster management software, establishing an association matrix between GPU units and generating a topology grouping set; based on the topology detection analysis, calculating the communication performance index, constructing a topology graph model and determining the optimal communication path of the multi-card task; combining the multi-objective optimization algorithm to generate a perception scheduling strategy and selecting the optimal GPU combination from the grouping set; in the task execution process, dynamically adjusting the scheduling strategy according to the real-time cluster state and task performance data, maintaining the continuous adaptation of the physical topology structure and the task demand, and finally realizing the efficient collaborative scheduling of cross-cluster heterogeneous GPU resources. The application improves the scheduling efficiency of heterogeneous GPU resources and the overall performance of the system in the distributed computing scenario.

Device and environmental data are collected from user devices participating in communication sessions of a communications platform. The device and environmental data includes at least one of hardware specifications, firmware versions, processor utilization, memory utilization, ambient noise levels, or device statuses. A device-related or environmental issue is identified based on the device and environmental data. A notification is generated in response to identifying the device-related or environmental issue.

The invention discloses a bare-computer testing system and method for a server, and relates to the technical field of bare-computer testing for the server, and the bare-computer testing system comprises a hardware supervision platform, a bare-computer testing module and a visual decision-making module.The bare-computer testing system is provided with the hardware supervision platform, the bare-computer testing module and the visual decision-making module; a perfect bare computer testing system is constructed, the coverage integrity is improved, intelligent decision making is achieved, and the hardware specification compliance is automatically judged through a consistency checking engine; according to the method, report standardization is achieved, a machine-readable JSON / XML format test report is output, the problems of efficiency, accuracy, compatibility and intelligence in the large-scale server test process are solved, real-time monitoring processing can be conveniently conducted on each link, server bare computer test data and corresponding analysis results are managed, visualized and stored, and the test efficiency is improved. The bare computer test management of the server can be realized through cloud management and control of the Internet of Things, and the intelligent level of the bare computer test management of the server is improved.

This invention discloses a method and system for predicting the performance of convolutional neural networks (CNNs) based on hardware interaction features, relating to the fields of artificial intelligence and computer system performance optimization. The method involves obtaining hierarchical features of a CNN model; calculating interaction features based on these hierarchical features and the hardware specifications of the target hardware platform; using a Transformer model with a bias matrix for prediction; generating a hardware-aware bias matrix after processing the interaction features by a hardware-aware attention bias module, and then inputting this matrix into the Transformer encoder; and finally, after sequential processing by an input encoder, a Transformer encoder, and a linear decoding layer, predicting the runtime of the CNN model. This invention predicts the inference and training time of a CNN model through static analysis of its architecture and target hardware specifications.

The application discloses a kind of high-efficiency automatic tuning methods for dynamic shape model reasoning, the tuning method includes: step one, one or more to be tuned tensor program is normalized and is processed, is converted into the standard form conforming to preset scheduling template;Step two, including standardization tensor program, program feature information and target hardware specification information is passed to tuning engine and optimization is started;Step three, tuning engine receives including the bucketing factor obtained by bucketing strategy and the TopK value indicating the optimal performance of the first K configuration parameters, allocates scheduling template and configuration parameter transmitter, cooperates, and optimizes each barrel corresponding subproblem, obtains the corresponding optimization kernel in each barrel range to complete automatic tuning.The application also discloses a tuning system for implementing the above method, which has a wide range of application scenarios.

The invention discloses a method for rapidly solving an optimal VCOM value, and relates to the technical field of display panel calibration, and the method comprises the following specific steps: parameter configuration and sampling point generation: obtaining a VCOM effective adjustment range defined by a panel driving IC hardware specification, recording the minimum value of the range as a minimum value, recording the maximum value as a maximum value, and generating a sampling point; uniformly dividing the obtained interval into five continuous sub-intervals according to a fixed step length rule, and generating six sampling points; according to the invention, the effective adjustment range of the VCOM is determined by acquiring the hardware specification file of the panel drive IC, and the intervals are uniformly divided according to the fixed step length to generate the sampling points, so that the number of the sampling points is greatly reduced, the operation steps are simplified, and the calibration efficiency is effectively improved.

Relatively simple user input is used to build an entire application. Input data defines a set of specifications for an automated machine. The set of specifications includes one or more of hardware specifications, operating systems, software frameworks, runtime environments, network connectivity, and environmental considerations. The input data is analyzed to retrieve relevant information from a large corpus including a plurality of libraries. One or more prompts are generated based on the relevant information, and the one or more prompts are used to generate programmable logic controller (PLC) code including a sequence of operations for controlling the automated machine.

In summary, this technology generally relates to a method for training a machine learning (ML) model to perform semantic image segmentation, and to a computer-based method and apparatus for performing semantic image segmentation using the trained ML model. The training method enables the semantic image segmentation ML model to make predictions faster without significant loss of accuracy. The training method also enables the ML model to be implemented on devices with different hardware specifications, such as different computing power and memory.

This disclosure proposes a hardware-based parameter tuning method, apparatus, electronic device, and storage medium. The method includes: obtaining a target kernel function to be optimized; automatically identifying at least one adjustable parameter in the target kernel function in response to the target kernel function; determining a candidate value range for each adjustable parameter based on the hardware specifications of the target hardware platform executing the target kernel function; pruning the candidate value range corresponding to each adjustable parameter based on the scale information of the data to be processed to generate a candidate configuration space; and performing a search in the candidate configuration space to determine the optimal execution configuration of the target kernel function. This disclosure can automatically construct an efficient and feasible tuning configuration space, reducing the threshold for tuning and improving tuning efficiency.