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A Multi-Level-Optimization Framework for FPGA-Based Cellular Neural Network Implementation

Authors:

Cheng ZhuoAuthors Info & Claims

ACM Journal on Emerging Technologies in Computing Systems (JETC), Volume 14, Issue 4

Article No.: 47, Pages 1 - 17

https://doi.org/10.1145/3273957

Published: 28 November 2018 Publication History

Abstract

Cellular Neural Network (CeNN) is considered as a powerful paradigm for embedded devices. Its analog and mix-signal hardware implementations are proved to be applicable to high-speed image processing, video analysis, and medical signal processing with its efficiency and popularity limited by smaller implementation size and lower precision. Recently, digital implementations of CeNNs on FPGA have attracted researchers from both academia and industry due to its high flexibility and short time-to-market. However, most existing implementations are not well optimized to fully utilize the advantages of FPGA platform with unnecessary design and computational redundancy that prevents speedup. We propose a multi-level-optimization framework for energy-efficient CeNN implementations on FPGAs. In particular, the optimization framework is featured with three level optimizations: system-, module-, and design-space-level, with focus on computational redundancy and attainable performance, respectively. Experimental results show that with various configurations our framework can achieve an energy-efficiency improvement of 3.54× and up to 3.88× speedup compared with existing implementations with similar accuracy.

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Index Terms

A Multi-Level-Optimization Framework for FPGA-Based Cellular Neural Network Implementation
1. Hardware
  1. Emerging technologies
    1. Analysis and design of emerging devices and systems
      1. Emerging architectures
  2. Integrated circuits
    1. Reconfigurable logic and FPGAs

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Information & Contributors

Information

Published In

cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 14, Issue 4

Special Issue on Neuromorphic Computing

October 2018

164 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/3294068

Editor:
Yuan Xie
University of California, Santa Barbara, USA

Issue’s Table of Contents

Copyright © 2018 ACM.

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 28 November 2018

Accepted: 01 August 2018

Revised: 01 May 2018

Received: 01 December 2017

Published in JETC Volume 14, Issue 4

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https://doi.org/10.1016/j.engappai.2024.108221
Liu ZZhang QXie DZhang MLi XZhong HLi GHe MShang DWang CGu LYang GJin KGe C(2023)Interface-type tunable oxygen ion dynamics for physical reservoir computingNature Communications10.1038/s41467-023-42993-x14:1Online publication date: 7-Nov-2023
https://doi.org/10.1038/s41467-023-42993-x
Zhang FShi HJi’e MWang LDuan S(2022)Dynamic Effects of a MCNN-CS Under Electromagnetic Induction and Its ApplicationInternational Journal of Bifurcation and Chaos10.1142/S021812742250165632:11Online publication date: 26-Sep-2022
https://doi.org/10.1142/S0218127422501656
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