Title : 
C-Mine: Data mining of logic common cases for low power synthesis of Better-Than-Worst-Case designs
         
        
            Author : 
Chen-Hsuan Lin ; Lu Wan ; Deming Chen
         
        
            Author_Institution : 
Dept. of ECE, UIUC, Champaign, IL, USA
         
        
        
        
        
        
            Abstract : 
The Better-Than-Worst-Case (BTW) design methodology is well-known for its potential to improve circuit energy efficiency, performance, and reliability. However, most existing methods do not provide sufficiently scalable solutions. Thus, we propose a new technique, C-Mine, which combines two scalable techniques, data mining and SAT solving, to provide scale-up solutions. Data mining can efficiently extract patterns from an enormous data set, and SAT solving is famous for its scalable verification. The experimental results show that, compared to a recent publication, C-Mine can achieve compatible performance with an additional 5% energy savings, and 50x speedup for bigger benchmarks on average.
         
        
            Keywords : 
circuit reliability; data mining; logic circuits; C-Mine; SAT solving; better-than-worst-case designs; data mining; logic common cases; low power synthesis; reliability; scalable techniques; Data mining; Data models; Delays; Indexes; Optimization; Throughput; Common case; Data Mining; Energy efficiency; Resynthesis; SAT solving; Scalability; Timing error resilience;
         
        
        
        
            Conference_Titel : 
Design Automation Conference (DAC), 2014 51st ACM/EDAC/IEEE
         
        
            Conference_Location : 
San Francisco, CA
         
        
        
            DOI : 
10.1145/2593069.2593107