Automated error prediction for approximate sequential circuits

Amrut Kapare, Hari Cherupalli, John M Sartori

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Synthesis tools for approximate sequential circuits require the ability to quickly, efficiently, and automatically characterize and bound the errors produced by the circuits. Previous approaches to characterize errors in approximate sequential circuits have been based on simulations spanning all cycles of a sequential computation. These approaches, however, are not scalable and only accommodate small circuit modules and short computation times. In this paper, we observe that the statistical properties of errors in many approximate sequential circuits follow patterns that can be easily learned. Therefore, statistical error characteristics of these approximate sequential circuits can be predicted with high accuracy using only a few cycles of characterization data. Based on this novel observation, we propose a methodology for predicting error statistics in approximate sequential circuits that is accurate, fully automated, and has significantly lower overhead than prior approaches. Our methodology is robust to changes in predicted error metrics, circuit input distributions, and types of approximate hardware modules used in approximate circuits. We demonstrate the accuracy and scalability of our approach over a range of sequential circuits. On average, prediction inaccuracy is less than 2% and error characterization time is reduced by 99% compared to a simulation-based approach.

Original languageEnglish (US)
Title of host publication2016 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450344661
StatePublished - Nov 7 2016
Event35th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016 - Austin, United States
Duration: Nov 7 2016Nov 10 2016

Publication series

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


Other35th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016
CountryUnited States

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