Efficient hardware acceleration of CNNs using logarithmic data representation with arbitrary log-base

Sebastian Vogel, Mengyu Liang, Andre Guntoro, Walter Stechele, Gerd Ascheid

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

39 Scopus citations

Abstract

Efficient acceleration of Deep Neural Networks is a manifold task. In order to save memory requirements and reduce energy consumption we propose the use of dedicated accelerators with novel arithmetic processing elements which use bit shifts instead of multipliers. While a regular power-of-2 quantization scheme allows for multiplierless computation of multiply-accumulate-operations, it suffers from high accuracy losses in neural networks. Therefore, we evaluate the use of powers-of-arbitrary-log-bases and confirmed their suitability for quantization of pre-trained neural networks. The presented method works without retraining of the neural network and therefore is suitable for applications in which no labeled training data is available. In order to verify our proposed method, we implement the log-based processing elements into a neural network accelerator on an FPGA. The hardware efficiency is evaluated in terms of FPGA utilization and energy requirements in comparison to regular 8-bit-fixed-point multiplier based acceleration. Using this approach hardware resources are minimized and power consumption is reduced by 22.3%.

Original languageEnglish
Title of host publication2018 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - Digest of Technical Papers
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450359504
DOIs
StatePublished - 5 Nov 2018
Event37th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - San Diego, United States
Duration: 5 Nov 20188 Nov 2018

Publication series

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

Conference

Conference37th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018
Country/TerritoryUnited States
CitySan Diego
Period5/11/188/11/18

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