Non-volatile in-memory computing chip and operation control method thereof

Abstract

The present invention provides a non-volatile in-memory computing chip and an operation control method thereof. The non-volatile in-memory computing chip includes: a cache module for caching data; a non-volatile in-memory computing module connected to the cache The module is used to perform operations on the data sent by the cache module; the post-processing module is connected to the non-volatile in-memory computing module, and is used to post-process the calculation results of the non-volatile in-memory computing module; wherein, The non-volatile in-memory computing module includes: a non-volatile memory cell array, a row-column decoder connected to the non-volatile memory cell array, and a read-write circuit connected to the non-volatile memory cell array. Among them, through the above-mentioned non-volatile in-memory computing chip combined with the operation control method, the multiplication and accumulation operation and the binary neural network operation are realized based on the integrated storage and calculation technology, without the need to transmit data between the memory and the processor, reducing power consumption and time. delay.

Description

technical field [0001] The invention relates to the technical field of application of semiconductor integrated circuits, in particular to a non-volatile in-memory computing chip and an operation control method thereof. 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 1 or -1. Through the...

AI Technical Summary

Problems solved by technology

[0004] However, although the phase transition between the binary neural network and the floating-point neural network can reduce the storage space occupied and reduce the operation time, however, 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

Images