Fault-Tolerant In-Memory Crossbar Computing Using Quantified Constraint Solving
Abstract
There has been a surge of interest in the effective storage and computation of data using nanoscale crossbars. In this paper, we present a new method for automating the design of fault-Tolerant crossbars that can effectively compute Boolean formula. Our approach leverages recent advances in Satisfiability Modulo Theories (SMT) solving for quantified bit-vector formula (QBVF). We demonstrate that our method is well-suited for fault-Tolerant computation and can perform Boolean computations despite stuck-open and stuck-closed interconnect defects as well as wire faults. We employ our framework to generate various arithmetic and logical circuits that compute correctly despite the presence of stuck-At faults as well as broken wires.
Publication Date
12-14-2015
Publication Title
Proceedings of the 33rd IEEE International Conference on Computer Design, ICCD 2015
Number of Pages
101-108
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/ICCD.2015.7357090
Copyright Status
Unknown
Socpus ID
84962440870 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84962440870
STARS Citation
Velasquez, Alvaro and Jha, Sumit Kumar, "Fault-Tolerant In-Memory Crossbar Computing Using Quantified Constraint Solving" (2015). Scopus Export 2015-2019. 1739.
https://stars.library.ucf.edu/scopus2015/1739