Learning Boolean circuits from examples for approximate logic synthesis
File(s)
Accepted version
Author(s)
Boroumand, Sina
Bouganis, Christos
Constantinides, George
Type
Conference Paper
Abstract
Many computing applications are inherently error resilient. Thus,it is possible to decrease computing accuracy to achieve greater effi-ciency in area, performance, and/or energy consumption. In recentyears, a slew of automatic techniques for approximate computinghas been proposed; however, most of these techniques require fullknowledge of an exact, or ‘golden’ circuit description. In contrast,there has been significant recent interest in synthesizing computa-tion from examples, a form of supervised learning. In this paper, weexplore the relationship between supervised learning of Booleancircuits and existing work on synthesizing incompletely-specifiedfunctions. We show that when considered through a machine learn-ing lens, the latter work provides a good training accuracy butpoor test accuracy. We contrast this with prior work from the 1990swhich uses mutual information to steer the search process, aimingfor good generalization. By combining this early work with a recentapproach to learning logic functions, we are able to achieve a scal-able and efficient machine learning approach for Boolean circuitsin terms of area/delay/test-error trade-off.
Date Issued
2021-01-18
Date Acceptance
2020-09-12
Citation
2021 26th Asia and South Pacific Design Automation Conference (ASP-DAC), 2021, pp.524-529
Publisher
ACM
Start Page
524
End Page
529
Journal / Book Title
2021 26th Asia and South Pacific Design Automation Conference (ASP-DAC)
Copyright Statement
© 2021 Association for Computing Machinery. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 2021 26th Asia and South Pacific Design Automation Conference (ASP-DAC), 18 Jan 2021, https://doi.org/10.1145/3394885.3431559
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/P010040/1
EP/S030069/1
Source
26th Asia and South Pacific Design Automation Conference - ASP-DAC 2021
Subjects
Science & Technology
Technology
Computer Science, Hardware & Architecture
Computer Science, Software Engineering
Computer Science
logic synthesis
information theory
approximate computing
Publication Status
Published
Start Date
2021-01-18
Finish Date
2021-01-21
Coverage Spatial
Virtual
Date Publish Online
2021-01-29