C-mine: Data mining of logic common cases for improved timing error resilience with energy efficiency

Chen Hsuan Lin, Lu Wan, Deming Chen

Research output: Contribution to journalArticlepeer-review

Abstract

The better-than-worst-case (BTW) design methodology can achieve higher circuit energy efficiency, performance, or reliability by allowing timing errors for rare cases and rectifying them with error correction mechanisms. Therefore, the performance of BTW design heavily depends on the correctness of common cases, which are frequent input patterns in a workload. However, most existing methods do not provide sufficiently scalable solutions and also overlook the whole picture of the design. Thus, we propose a new technique, common-case mining method (C-Mine), which combines two scalable techniques, data mining and Boolean satisfiability (SAT) solving, to overcome these limitations. Data mining can efficiently extract patterns from an enormous dataset, and SAT solving is famous for its scalable verification. In this article, we present two versions of C-Mine, C-Mine-DCT and C-Mine-APR, which aim at faster runtime and better energy saving, respectively. The experimental results show that, compared to a recent publication, C-Mine-DCT can achieve compatible performance with an additional 8% energy savings and 54x speedup for bigger benchmarks on average. Furthermore, C-Mine-APR can achieve up to 13% more energy saving than C-Mine-DCT while confronting designs with more common cases.

Original languageEnglish (US)
Article number20
JournalACM Transactions on Design Automation of Electronic Systems
Volume23
Issue number2
DOIs
StatePublished - Nov 2017

Keywords

  • Common cases
  • Data mining
  • Energy efficiency
  • Resynthesis
  • SAT solving
  • Scalability
  • Timing error resilience

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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