Hypergraph-Cover Diversity For Maximally-Resilient Reconfigurable Systems

Keywords

Area Efficiency; Design Diversity; Fault Tolerance; FPGAs; Hypergraphs; Reconfigurable Systems; Reliability

Abstract

Scaling trends of reconfigurable hardware (RH) and their design flexibility have proliferated their use in dependability-critical embedded applications. Although their reconfigurability can enable significant fault tolerance, due to the complexity of execution time in their design flow, in-field reconfigurability can be infeasible and thus limit such potential. This need is addressed by developing a graph and set theoretic approach, named hypergraph-cover diversity (HCD), as a preemptive design technique to shift the dominant costs of resiliency to design-time. In particular, union-free hypergraphs are exploited to partition the reconfigurable resources pool into highly separable subsets of resources, each of which can be utilized by the same synthesized application netlist. The diverse implementations provide reconfiguration-based resilience throughout the system lifetime while avoiding the significant overheads associated with runtime placement and routing phases. Two novel scalable algorithms to construct union-free hypergraphs are proposed and described. Evaluation on a Motion-JPEG image compression core using a Xilinx 7-series-based FPGA hardware platform demonstrates a statistically significant increase in fault tolerance and area efficiency when using proposed work compared to commonly-used modular redundancy approaches.

Publication Date

11-23-2015

Publication Title

Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015

Number of Pages

1086-1092

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/HPCC-CSS-ICESS.2015.294

Socpus ID

84961755604 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84961755604

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