Computing array based neural network processor

Abstract

The present invention provides a neural network processor. The processor comprises at least one computing unit consisting of a host processor and a computing array, wherein the computing array is organized into a row and column two-dimensional matrix by a plurality of processing units, the host processor controls to load neuron data and weight values into the computing array, and the processing unit carries out multiply-accumulate operation on the received neuron data and weight values and passes neuron data and weight values in different directions to a next-level processing unit; and a control unit, for controlling the computing unit to carry out relevant computation of a neural network. By virtue of the computing device provided by the present invention, the computation speed of the neural network can be accelerated and the bandwidth requirements in the computation process can be reduced.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a neural network processor based on a computing array. Background technique [0002] Deep learning is an important branch of the field of machine learning, which has made major breakthroughs in recent years. The neural network model trained with deep learning algorithms has achieved remarkable results in image recognition, speech processing, intelligent robots and other application fields. [0003] The deep neural network simulates the neural connection structure of the human brain by building a model, and describes the data features hierarchically through multiple transformation stages when processing signals such as images, sounds, and texts. With the continuous improvement of the complexity of the neural network, the neural network technology has many problems in the actual application process, such as occupying a lot of resources, slow operation speed, and la...

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Problems solved by technology

[0004] However, since the neural network processor is a computationally intensive and memory-intensive processor, on the one hand, the neural network model includes a large number of multiplication and addition operations and other nonlinear operations, which require the neural network processor to maintain high load operation to ensure the neural network The computing requirements of the network model; on the other hand, there are a large number of parameter iterations in the neural network computing process, and the computing unit needs to access a lot of memory, which greatly increases the bandwidth design requirements of the processor and increases memory access power consumption.

[0005] Therefore, it is necessary to improve the existing neural network processors to solve the problems of high computing circuit hardware overhead and large on-chip memory access bandwidth.

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