Title

Self-Healing Reconfigurable Logic Using Autonomous Group Testing

Keywords

Autonomous systems; Evolvable hardware; Group testing; Organic computing; Reconfigurable architectures; Reliable systems

Abstract

A group-testing-based fault resolution is incorporated into SRAM-based reconfigurable Field Programmable Gate Arrays (FPGAs) to provide an evolvable hardware system with self-healing and self-organizing properties. The proposed approach employs adaptive group testing techniques to autonomously maintain FPGA resource viability information as an organic means of transient and permanent fault resolution. Reconfigurability of the SRAM-based FPGA is leveraged to locate faulty logic resources which are successively excluded by group testing using alternate device configurations. This simplifies the system architect's role to definition of functionality using a high-level Hardware Description Language (HDL) and system-level performance vs. availability operating point. System availability, throughput, and mean time to isolate faults are monitored and maintained using an observer-controller model. The proposed group testing method operates on the output response produced for real-time operational inputs, which eliminates the need for dedicated test vectors. The proposed system was demonstrated using a Data Encryption Standard (DES) core on 4-input and 6-input LUT-based Xilinx FPGA models. With a single simulated stuck-at fault, the system identifies a completely validated replacement configuration within a few test stages. Results also include approaches for optimizing group size, resource redundancy, and availability. The approach demonstrates a readily-implemented yet robust organic hardware application that features a high degree of autonomous self-control. © 2012 Elsevier B.V. All rights reserved.

Publication Date

4-12-2013

Publication Title

Microprocessors and Microsystems

Volume

37

Issue

2

Number of Pages

174-184

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.micpro.2012.09.009

Socpus ID

84875926528 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84875926528

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