Shanghai Cambricon Information Technology Co., Ltd

Cambricon Technologies Corporation Limited

Chinese Academy of Sciences

Cambricon (xi'an) Semiconductor Co., Ltd.

Zidong Du

Shaoli Liu

Lei Zhang

Tianshi Chen

Yao Zhang

Xuda Zhou

Qi Guo

Zai Wang

Shengyuan Zhou

Daofu Liu

Yunji Chen

Tian Zhi

Shuai Hu

Xiaofu Meng

Zhou Fang

Yifan Hao

Jie Wei

Xiaobing Chen

Zhenyu Su

Yuzhe Luo

Qun Liu

Wei Li

Xiaobin Chen

Yongzhe Sun

Yixuan Ren

Luyang Jin

Xiaoyong Zhou

Dingfei Zhang

Chongwen Li

The present disclosure provides a processing device including: a coarse-grained pruning unit configured to perform coarse-grained pruning on a weight of a neural network to obtain a pruned weight, an operation unit configured to train the neural network according to the pruned weight. The coarse-grained pruning unit is specifically configured to select M weights from the weights of the neural network through a sliding window, and when the M weights meet a preset condition, all or part of the M weights may be set to 0. The processing device can reduce the memory access while reducing the amount of computation, thereby obtaining an acceleration ratio and reducing energy consumption.

Processing method and accelerating device

The application provides a processor and processing method. The processor includes a task segmentation device configured to perform task segmentation according to a task segmentation granularity and a hardware resource division device configured to divide hardware resources of the processor according to a task segmentation result. The processor and processing method provided by the application improve the processing performance and reduce the overhead by performing task segmentation and configuring different hardware according to task segmentation.

Data sharing system and data sharing method therefor

The application provides an operation method and device. Quantized data is looked up to realize an operation, which simplifies the structure and reduces the computation energy consumption of the data, meanwhile, a plurality of operations are realized.

Processing method and device, operation method and device

An allocation system for machine learning, comprising a terminal server and a cloud server. The terminal server is used for: acquiring demand information; generating a control instruction according to the demand information, wherein the control instruction comprises a terminal control instruction and a cloud control instruction; parsing the terminal control instruction to obtain a terminal control signal; and calculating a terminal workload of a machine learning algorithm of each stage according to the terminal control signal to obtain a terminal computation result. The cloud server is used for parsing the cloud control instruction to obtain a cloud control signal, and calculating a cloud workload of the machine learning algorithm of each stage according to the cloud control signal to obtain a cloud computation result. The terminal computation result and the cloud computation result together compose an output result.

Allocation system, method and apparatus for machine learning, and computer device

The application provides a processing method and device. Weights and input neurons are quantized respectively, and a weight dictionary, a weight codebook, a neuron dictionary, and a neuron codebook are determined. A computational codebook is determined according to the weight codebook and the neuron codebook. Meanwhile, according to the application, the computational codebook is determined according to two types of quantized data, and the two types of quantized data are combined, which facilitates data processing.

Processing method and device

The present disclosure provides a neural network processing system that comprises a multi-core processing module composed of a plurality of core processing modules and for executing vector multiplication and addition operations in a neural network operation, an on-chip storage medium, an on-chip address index module, and an ALU module for executing a non-linear operation not completable by the multi-core processing module according to input data acquired from the multi-core processing module or the on-chip storage medium, wherein the plurality of core processing modules share an on-chip storage medium and an ALU module, or the plurality of core processing modules have an independent on-chip storage medium and an ALU module. The present disclosure improves an operating speed of the neural network processing system, such that performance of the neural network processing system is higher and more efficient.

Method and system for processing neural network

Disclosed are a scheduling method and a related apparatus. A computing apparatus in a server can be chosen to implement a computation request, thereby improving the running efficiency of the server.

Scheduling method and related apparatus

The present disclosure provides a neural network processor and neural network computation method that deploy a memory and a cache to perform a neural network computation, where the memory may be configured to store data and instructions of the neural network computation, the cache may be connected to the memory via a memory bus, thereby, the actual compute ability of hardware may be fully utilized, the cost and power consumption overhead may be reduced, parallelism of the network may be fully utilized, and the efficiency of the neural network computation may be improved.

Neural network processor and neural network computation method

A computing device, comprising: a computing module, comprising one or more computing units; and a control module, comprising a computing control unit, and used for controlling shutdown of the computing unit of the computing module according to a determining condition. Also provided is a computing method. The computing device and method have the advantages of low power consumption and high flexibility, and can be combined with the upgrading mode of software, thereby further increasing the computing speed, reducing the computing amount, and reducing the computing power consumption of an accelerator.

Apparatus and method for sparse training acceleration in neural networks

Computing device and method

Growing community of inventors

Pudong New Area, China

Zidong Du