research-article

A novel data format for approximate arithmetic computing

Authors:

Akshaya S Kankanhalli Nagendra,

Gang QuAuthors Info & Claims

2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC)

Pages 390 - 395

https://doi.org/10.1109/ASPDAC.2017.7858354

Published: 16 January 2017 Publication History

Abstract

Approximate computing has become one of the most popular computing paradigms in the era of the Internet of things and big data. It takes advantages of the error-tolerable feature of many applications, such as machine learning and image/signal processing, to reduce the resource required to deliver certain level of computation quality. In this paper, we propose an approximate integer format (AIF) and its associated arithmetic operations for energy minimization with controllable computation accuracy. In AIF, operands are segmented at run time such that the computation is performed only on part of operands by computing units (such as adders and multipliers) of smaller bit-width. The proposed AIF can be used for any arithmetic operation and can be extended to fixed point numbers. It can also be incorporated into higher level design such as architectural and programming language to give user the control of approximate computing. Experimental results show that our AIF based approximation computing approach can achieve high accuracy, incurs very little additional overhead, and save considerable energy.

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Cited By

Li HPang YZhang JChen YZhirnov VSasan ASavidis I(2021)Security Enhancements for Approximate Machine LearningProceedings of the 2021 Great Lakes Symposium on VLSI10.1145/3453688.3461753(461-466)Online publication date: 22-Jun-2021
https://dl.acm.org/doi/10.1145/3453688.3461753
Wang YXu QQu GDong JHomayoun HTaskin BMohsenin TZhao W(2019)Information Hiding behind Approximate ComputationProceedings of the 2019 Great Lakes Symposium on VLSI10.1145/3299874.3319456(405-410)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3299874.3319456

Index Terms

A novel data format for approximate arithmetic computing
1. Hardware
  1. Integrated circuits
    1. Logic circuits
      1. Arithmetic and datapath circuits

Index terms have been assigned to the content through auto-classification.

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cover image Guide Proceedings

2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC)

Jan 2017

786 pages

Copyright © 2017.

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IEEE Press

Publication History

Published: 16 January 2017

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Cited By

Li HPang YZhang JChen YZhirnov VSasan ASavidis I(2021)Security Enhancements for Approximate Machine LearningProceedings of the 2021 Great Lakes Symposium on VLSI10.1145/3453688.3461753(461-466)Online publication date: 22-Jun-2021
https://dl.acm.org/doi/10.1145/3453688.3461753
Wang YXu QQu GDong JHomayoun HTaskin BMohsenin TZhao W(2019)Information Hiding behind Approximate ComputationProceedings of the 2019 Great Lakes Symposium on VLSI10.1145/3299874.3319456(405-410)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3299874.3319456

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