Paper Info
Title
C-Mine: Data Mining of Logic Common Cases for Improved Timing Error Resilience with Energy Efficiency.
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.
Year
DOI
Venue
2018
10.1145/3144534
ACM Trans. Design Autom. Electr. Syst.
Keywords
Field
DocType
Data mining, SAT solving, common cases, energy efficiency, resynthesis, scalability, timing error resilience
Data mining,Efficient energy use,Computer science,Workload,Correctness,Boolean satisfiability problem,Design methods,Error detection and correction,Scalability,Speedup
Journal
Volume
Issue
ISSN
23
2
1084-4309
Citations 
PageRank 
References 
1
0.35
18
Authors
3
Name
Order
Citations
PageRank
Chen-Hsuan Lin1626.90
Lu Wan21347.39
Deming Chen31432127.66