Deep neural network reasoning method and computing device

Abstract

The present application relates to the field of neural networks, in particular to a deep neural network reasoning method and a computing device. The method includes receiving input features inputted to an operation layer in a first depth neural network model; determining an index value corresponding to the operation layer; determining a codebook of the operation layer according to the index valueof the operation layer; quantifying the input features according to a preset first quantization rule; In the operation layer, the operation layer performs the operation on the input features accordingto the codewords corresponding to the quantized input features in the quantized input features query codebook. In accordance with an embodiment of that present application, each quantization model parameter and each quantization input characteristic are multiplied to obtain a codebook, as long as the actual input feature is quantized into the quantized input feature, the floating-point multiplication result of the quantized input feature can be obtained by directly consulting the codebook, so that the operation can be completed quickly, and the reasoning speed of the deep neural network can be greatly accelerated without the substantial floating-point multiplication operation.

Description

technical field [0001] The present application relates to the field of neural networks, in particular to a deep neural network reasoning method and computing equipment. Background technique [0002] With the rapid development and popularization of computer and information technology, industry application data is growing explosively. Industry and enterprise big data that can reach several terabytes (TB for short) or even petabytes (PB for short) often imply a lot of in-depth knowledge and value that are not available when the amount of data is small , Data analysis led by large-scale machine learning (including deep learning) is a key technology for converting big data into useful knowledge. As a key technology leading the development direction of artificial intelligence (AI), deep neural network has made remarkable achievements in many fields such as face recognition, image classification, target detection, video analysis, speech recognition, machine translation, etc. As a...

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present application provides a deep neural network reasoning method and computing equipment to solve the problem that the acceleration effect of the current deep neural network is difficult to meet the reasoning speed requirements of actual scenarios such as unmanned driving

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