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Hardware Architecture and Computational Flow of Binary Weighted Convolutional Neural Network Accelerator

A binary weight convolution and neural network technology, which is applied to the hardware architecture and calculation process of the binary weight convolution neural network dedicated accelerator, to achieve the effects of reducing access, improving efficiency, and reducing power consumption

Active Publication Date: 2020-04-17
南京风兴科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention aims to solve the technical problem of applying convolutional neural networks to power-constrained embedded systems, or at least propose a useful commercial option

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  • Hardware Architecture and Computational Flow of Binary Weighted Convolutional Neural Network Accelerator
  • Hardware Architecture and Computational Flow of Binary Weighted Convolutional Neural Network Accelerator
  • Hardware Architecture and Computational Flow of Binary Weighted Convolutional Neural Network Accelerator

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Embodiment Construction

[0039] Embodiments of the invention are described in detail below, examples of which are illustrated in the accompanying drawings. Firstly, the necessary overall hardware architecture is introduced, and then the optimized calculation process based on this hardware architecture is introduced. The implementations described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limitations of the present invention.

[0040] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "vertical", "horizontal" etc. The orientation or positional relationship is only a simplified description for the convenience of describing the present invention, and does not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation...

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Abstract

The invention discloses the hardware architecture of a binary weight convolution neural network accelerator and a calculation process thereof. The hardware architecture comprises three double-ended on-chip static random access memories which are used for buffering the binary weight of input neurons and a convolution layer, four convolution processing units capable of controlling calculation parts to complete major convolution calculation operation according to the calculation process, a feature map accumulation unit and a convolutional accumulation array. The feature map accumulation unit and the convolutional accumulation array are used for further processing the operation result of the convolution processing units to acquire a final correct output neuron value. The entire design exchanges data with an off-chip memory via a dynamic random access memory interface. In addition to the hardware architecture, the invention further provides the detailed calculation process which optimizes the hardware architecture and uses four lines of input feature map as a complete calculation unit. According to the invention, input data are reused to the greatest extent; the access of the off-chip memory is eliminated as much as possible; the power consumption of the deep binary convolution neural network calculation can be effectively reduced; a deep network is supported; and the scheme provided by the invention is a reasonable scheme which can be applied to an embedded system of visual application.

Description

technical field [0001] The invention designs the field of computer and electronic information technology, and in particular relates to a hardware architecture and a calculation process of a special accelerator for a binary weighted convolutional neural network. Background technique [0002] The deep convolutional neural network model has made great breakthroughs and successes in many fields such as image classification, action detection, speech recognition and other big data analysis tasks. On the one hand, as the effect of the convolutional neural network becomes better and better, its topology structure is also deepening, and the number of parameters has reached 10 to the 6th power and above, which brings extreme computational complexity. With a big boost, the required computing power has exploded. On the other hand, embedded systems can only provide limited resources, and their power consumption is also limited within a certain range. Although the existing solutions usin...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/063
CPCG06N3/063
Inventor 王中风王逸致林军
Owner 南京风兴科技有限公司
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