Hardware implementation of neural network

Abstract

A hardware implementation of a neural network and a method of processing data in such a hardware implementation are disclosed. Input data for a plurality of layers of the network is processed in blocks, to generate respective blocks of output data. The processing proceeds depth-wise through the plurality of layers, evaluating all layers of the plurality of layers for a given block, before proceeding to the next block.

Description

Background technique [0001] A deep neural network (DNN) is a type of artificial neural network that can be used in machine learning applications. In particular, DNNs can be used in signal processing applications, including image processing and computer vision applications. [0002] DNNs have been implemented in applications where power resource is not an important factor. Nonetheless, DNNs have applications in many different technical domains where the hardware resources used to implement DNNs are such that power consumption, processing power, or silicon area are constrained. Accordingly, there is a need to implement hardware configured to implement a DNN (or at least a portion thereof) in an efficient manner, eg, in a manner that requires less silicon area or less processing power to operate. Furthermore, DNNs can be configured in many different ways for a variety of different applications. Therefore, there is also a need for hardware for implementing DNNs that has the fle...

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Problems solved by technology

Nonetheless, DNNs have applications in many different technical domains where hardware resources for implementing DNNs are such that power consumption, processing power, or silicon area are constrained

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