Chip for realizing binary neural network based on nonvolatile in-memory calculation and method

Abstract

The invention provides a chip for realizing a binary neural network based on nonvolatile in-memory calculation and a method, and the chip comprises a nonvolatile operation module which is used for carrying out the matrix multiply-add operation on a first binary data packet received by the nonvolatile operation module and a second binary data packet pre-stored in the nonvolatile operation module, wherein the weight of the binary neural network is generally fixed during the reasoning process, and the input characteristics corresponding to each layer of neural network are generally changed alongwith the application. The weight of the binary neural network is used as the second binary data packet to be pre-stored in the nonvolatile operation module, and the input characteristics of the binaryneural network are loaded to the nonvolatile operation module, so that the matrix multiplication and addition operation can be realized in the nonvolatile operation module, and the problems of powerconsumption and time delay caused by data migration can be solved.

Description

technical field [0001] The invention relates to the technical field of application of semiconductor integrated circuits, in particular to a chip and a method for realizing a binary neural network based on non-volatile in-memory calculation. Background technique [0002] With the introduction of deep learning theory and the improvement of numerical computing equipment, deep learning neural network technology has developed rapidly and has been widely used in computer vision, natural language processing and other fields. Now the neural network generally adopts floating-point calculation, which requires a large storage space and a long operation time. [0003] A binary neural network (Binary Neural Network, BNN) refers to a neural network obtained by simultaneously binarizing the weight values ​​in its weight matrix and each activation function value (eigenvalue) on the basis of a floating-point neural network, namely : Binarize the weight value and activation function value to...

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Problems solved by technology

[0004] However, although the binary neural network can reduce the storage space and operation time compared with the floating-point neural network, because the binary neural network still needs to transmit data between the memory and the processor, frequent data movement is still Will bring higher power consumption and delay

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