On-chip learning neural network processor

Abstract

The invention discloses an on-chip learning neural network processor comprising a data interface module, a data preprocessing module, a data cache region module, a neuron weight cache region module, a random initialization module, a neural computing unit module, a neural network forward computing control module, an activation function module, a neural state controller module and a neural network learning algorithm control module. The neural state controller module controls all the unit modules to cooperatively work to perform neural network learning and reasoning. The neural computing unit module is designed by using general hardware acceleration computing for programmable control of the neural network computing type and computing scale. The assembly line technology is added in the design so that the data throughput and the computing speed can be greatly enhanced, the focus is put on optimization of the multiply-add units of the neural computing unit and the hardware area can be greatly reduced. Hardware mapping is performed on the neural network learning algorithm so that the neural network processor is enabled to perform on-chip learning and offline reasoning.

Description

technical field [0001] The present invention relates to the field of integrated circuits and the field of artificial intelligence, in particular to an on-chip learning neural network processor. Background technique [0002] In the field of machine learning and cognitive science, artificial neural network (ANN), referred to as neural network or neural network, is a mathematical model that imitates the structure and function of biological neural network (animal's central nervous system, especially the brain) or Computational models for estimating or approximating functions. An artificial neural network is similar to a biological neural network in that it can compute parts of a function collectively and in parallel without describing the specific tasks of each unit. [0003] In the current Internet big data background, artificial neural network (ANN)-based brain-inspired computing has been applied in big data processing and analysis, and artificial intelligence has made a majo...

AI Technical Summary

Problems solved by technology

As the underlying platform, GPU plays the role of computing acceleration, but power consumption, cost and area are the main factors restricting it

In contrast, FPGA programmable chips or artificial neural network dedicated chips have higher requirements for implanting servers, programming environments, and programming capabilities, and lack general potential, so they are not suitable for popularization.

Images