RNSiM: Efficient Deep Neural Network Accelerator Using Residue Number Systems

Arman Roohi, Mohammad Reza Taheri, Shaahin Angizi, Deliang Fan

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

1 Scopus citations

Abstract

In this paper, we propose an efficient convolutional neural network (CNN) accelerator design, entitled RNSiM, based on the Residue Number System (RNS) as an alternative for the conventional binary number representation. Instead of traditional arithmetic implementation that suffers from the inevitable lengthy carry propagation chain, the novelty of RNSiM lies in that all the data, including stored weights and communication/computation, are performed in the RNS domain. Due to the inherent parallelism of the RNS arithmetic, power and latency are significantly reduced. Moreover, an enhanced integrated intermodulo operation core is developed to decrease the overhead imposed by non-modular operations. Further improvement in systems’ performance efficiency is achieved by developing efficient Processing-in-Memory (PIM) designs using various volatile CMOS and non-volatile Post-CMOS technologies to accelerate RNS-based multiplication-and-accumulations (MACs). The RNSiM accelerator’s performance on different datasets, including MNIST, SVHN, and CIFAR-10, is evaluated. With almost the same accuracy to the baseline CNN, the RNSiM accelerator can significantly increase both energy-efficiency and speedup compared with the state-of-the-art FPGA, GPU, and PIM designs. RNSiM and other RNS-PIMs, based on our method, reduce the energy consumption by orders of 28-77× and 331-897× compared with the FPGA and the GPU platforms, respectively.

Original languageEnglish (US)
Title of host publication2021 40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665445078
DOIs
StatePublished - 2021
Event40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021 - Munich, Germany
Duration: Nov 1 2021Nov 4 2021

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Volume2021-November
ISSN (Print)1092-3152

Conference

Conference40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021
Country/TerritoryGermany
CityMunich
Period11/1/2111/4/21

Keywords

  • Accelerator
  • Convolutional neural network
  • Processing-in-Memory
  • residue number system

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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