HBUNN - Hybrid binary-unary neural network: Realizing a complete CNN on an FPGA

Sayed Abdolrasouol Faraji, Gaurav Singh, Kia Bazargan

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

1 Scopus citations

Abstract

Most modern neural networks use the basic Multiply-Accumulate (MAC) Operation in some form or another, and as networks get larger, the computational needs for these larger networks grow rapidly. Typically neural networks implemented on FPGAs use variable multipliers so that any weight can be used in the MAC, but this also forces the accelerator designer to store the weights of the model in off-chip memory (DRAM). Moreover, because of high computational power and high memory bandwidth requirements of today's CNNs, it is harder for FPGA platforms to deliver the best performance. In this paper, we propose a fully parallel-pipeline Hybrid Binary-Unary Neural Network (HBUNN) architecture to implement a low-cost and high-performance ResNet-18 convolutional neural network. We use a hybrid binary-unary method to implement constant-coefficient multipliers and batch normalization units. These two units reduce hardware cost by 30.7% and 47.97% on average compared to the conventional binary equivalent, respectively. Moreover, we propose a novel training scheme using our hardware cost-aware regularizers that not only improves the area cost of the proposed architecture and the conventional binary architecture by 59.3% and 76.7% respectively, but also maintains the same accuracy. Finally, we have implemented three trained networks using different regularizers. The proposed HBUNN architectures reduce the area cost by 30%, and the area × delay cost by 69% on average compared to the conventional binary architectures. The error rate of the proposed work is 12.93%, while its throughput is 278 Kfps.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Computer Design, ICCD 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages156-163
Number of pages8
ISBN (Electronic)9781538666487
DOIs
StatePublished - Nov 2019
Externally publishedYes
Event37th IEEE International Conference on Computer Design, ICCD 2019 - Abu Dhabi, United Arab Emirates
Duration: Nov 17 2019Nov 20 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Computer Design, ICCD 2019

Conference

Conference37th IEEE International Conference on Computer Design, ICCD 2019
CountryUnited Arab Emirates
CityAbu Dhabi
Period11/17/1911/20/19

Keywords

  • CNN accelerator
  • Cost-aware regularizer
  • Quantized neural network
  • Stochastic computing
  • Unary computing

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