Neural network calculation special circuit and related calculation platform and implementation method thereof

Abstract

The invention discloses a special circuit for a neural network and a related computing platform and implementation method thereof. The special circuit comprises: a data reading module which comprisesa feature map reading sub-module and a weight reading sub-module which are respectively used for reading feature map data and weight data from an on-chip cache to a data calculation module when a depthwise convolution operation is executed, wherein the feature map reading sub-module is also used for reading the feature map data from the on-chip cache to the data calculation module when executing pooling operation; a data calculation module which comprises a dwconv module used for executing depthwise convolution calculation and a pooling module used for executing pooling calculation; and a datawrite-back module which is used for writing a calculation result of the data calculation module back to the on-chip cache. The use of hardware resources is reduced by multiplexing the read logic andthe write-back logic of the two types of operations. The special circuit provided by the invention adopts a high-concurrency pipeline design, so that the computing performance can be further improved.

Description

technical field [0001] The present invention relates to the field of hardware architecture, in particular to a special circuit for neural network calculation and its related computing platform and implementation method. Background technique [0002] In recent years, methods based on artificial neural networks (ANN, Artificial Neural Network), especially convolutional neural networks (CNN, Convolutional Neural Network) have achieved great success in many applications. In the field of computer vision, especially for image classification problems, the introduction of CNN has greatly improved the accuracy of image classification. [0003] Although ANN-based methods have advanced performance, they require more computational and memory resources than traditional methods. Especially with the development of neural networks, large-scale neural networks have more and more layers and data volumes, and traditional CPU platforms cannot meet their practical needs. Therefore, using FPGA,...

AI Technical Summary

Problems solved by technology

[0003] Although ANN-based methods have advanced performance, they require more computational and memory resources than traditional methods

Especially with the development of neural networks, large-scale neural networks have more and more layers and data volumes, and the use of traditional CPU platforms can no longer meet their practical needs.

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