Nonvolatile in-memory computing chip and operation control method thereof

Abstract

The invention provides a nonvolatile in-memory computing chip and an operation control method thereof. The nonvolatile in-memory computing chip comprises a cache module which is used for caching the data; a nonvolatile in-memory calculation module which is connected with the cache module and is used for executing operation on the data sent by the cache module; a post-processing module which is connected with the non-volatile in-memory calculation module and used for post-processing the operation result of the non-volatile in-memory calculation module, wherein the nonvolatile in-memory computing module comprises a nonvolatile memory cell array, a row and column decoder connected with the nonvolatile memory cell array, and a read-write circuit connected with the nonvolatile memory cell array. According to the present invention, the non-volatile in-memory computing chip is matched with an operation control method, the multiply-accumulate-add operation and the binary neural network operation are achieved based on a storage and calculation integrated technology, the data transmission between a memory and a processor is not needed, and the power consumption and the time delay are reduced.

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