Data processing method, device and system, electronic equipment and storage medium

Abstract

The invention discloses a data processing method, device and system, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining a setting instruction, and setting a computing network according to the setting instruction, wherein the setting instruction is used for setting the data flow direction among the computing cores in the computing network; obtaining at least one characteristic value, and respectively inputting the at least one characteristic value into at least one initial calculation core in the calculation network; transmitting the characteristic value according to the data flow direction by taking the initial calculation core as a starting point; generating a calculation result based on the characteristic value and the corresponding weight value by using each calculation core; according to the method, the data flow direction among the computing cores in the computing network is set through the setting instruction, so that the data flows among different levels or among the same levels, and the whole computing network can be fully utilized no matter what shapes of network models are processed.

Description

technical field [0001] The present application relates to the technical field of deep learning, and in particular to a data processing method, a data processing device, a data processing system, electronic equipment, and a computer-readable storage medium. Background technique [0002] At present, the development of deep learning puts forward a very high demand for computing power. Various ASIC (Application Specific Integrated Circuit, application specific integrated circuit) architectures emerge in an endless stream. The more representative ones are: Google's tensor processing unit (TPU, Tensor Processing Unit), which is a custom ASIC chip designed for machine learning workloads. But the TPU is not a general-purpose processor, but a matrix processor dedicated to neural network workloads. Its main task is matrix processing, and the hardware designer of the TPU knows every step of the operation process. So they put tens of thousands of multipliers and adders and connect them...

AI Technical Summary

Problems solved by technology

However, the shape of the neural network or the number of specification parameters vary greatly, while the hardware scale of TPU and so on is fixed. Only when the shape of the network fully matches the chip can the utilization of hardware resources be maximized. The neural network, its hardware resource utilization is related to the size of the network, but it cannot fully utilize the hardware resources, and there is a problem of insufficient resource utilization

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