Accelerating convolutional neural network inference based on a reconfigurable sliced systolic array

Yixuan Zeng, Heming Sun, Jiro Katto, Yibo Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Convolutional neural networks (CNNs) have achieved great successes on many computer vision tasks, such as image recognition, video processing, and target detection. In recent years, many hardware designs have been devoted to accelerating CNN inference. In order to further speed up CNN inference and reduce data waste, this work proposed a reconfigurable sliced systolic array: 1) Depending on the number of network nodes in each layer, the slice mode could be dynamically configured to achieve high throughput and resource utilization. 2) To take full advantage of convolution reuse and weight reuse, this work designed a tile-column sliding (TCS) processing dataflow. 3) A four-stage for loop algorithm was employed, which divides the CNN calculation into several parts based on the input nodes and output nodes. The entire CNN inference is carried out using integer-only arithmetic originated from TensorLite. Experimental results prove that these strategies lead to significant improvement in inference performance and energy efficiency.

Original languageEnglish
Title of host publication2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728192017
DOIs
Publication statusPublished - 2021
Event53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Daegu, Korea, Republic of
Duration: 2021 May 222021 May 28

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2021-May
ISSN (Print)0271-4310

Conference

Conference53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021
Country/TerritoryKorea, Republic of
CityDaegu
Period21/5/2221/5/28

Keywords

  • Convolutional neural network
  • Deep learning accelerator
  • Systolic array

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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