research-article

A Deep Neural Network Accelerator using Residue Arithmetic in a Hybrid Optoelectronic System

Authors:

Yousra Alkabani,

Tarek El-GhazawiAuthors Info & Claims

ACM Journal on Emerging Technologies in Computing Systems (JETC), Volume 18, Issue 4

Article No.: 81, Pages 1 - 26

https://doi.org/10.1145/3550273

Published: 13 October 2022 Publication History

Abstract

The acceleration of Deep Neural Networks (DNNs) has attracted much attention in research. Many critical real-time applications benefit from DNN accelerators but are limited by their compute-intensive nature. This work introduces an accelerator for Convolutional Neural Network (CNN), based on a hybrid optoelectronic computing architecture and residue number system (RNS). The RNS reduces the optical critical path and lowers the power requirements. In addition, the wavelength division multiplexing (WDM) allows high-speed operation at the system level by enabling high-level parallelism. The proposed RNS compute modules use one-hot encoding, and thus enable fast switching between the electrical and optical domains. We propose a new architecture that combines residue electrical adders and optical multipliers as the matrix-vector multiplication unit. Moreover, we enhance the implementation of different CNN computational kernels using WDM-enabled RNS based integrated photonics. The area and power efficiency of the proposed accelerator are 0.39 TOPS/s/mm² and 3.22 TOPS/s/W, respectively. In terms of computation capability, the proposed chip is 12.7× and 4.02× better than other optical implementation and memristor implementation, respectively. Our experimental evaluation using DNN benchmarks illustrates that our architecture can perform on average more than 72 times faster than GPU under the same power budget.

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A Deep Neural Network Accelerator using Residue Arithmetic in a Hybrid Optoelectronic System

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cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 18, Issue 4

October 2022

429 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/3563906

Editor:
Ramesh Karri
Polytechnic Institute of New York University, USA

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Publication History

Published: 13 October 2022

Online AM: 21 July 2022

Accepted: 05 July 2022

Revised: 01 July 2022

Received: 28 December 2021

Published in JETC Volume 18, Issue 4

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