Scalable and Accelerated Self-healing Control Circuit Using Evolvable Hardware

Controllers are mission-critical components of any electronic design. By sending control signals, they decide which and when other data path elements must operate. Faults, especially Single Event Upset (SEU) occurrence in these components, can lead to functional/mission failure of the system when de...

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Veröffentlicht in:ACM transactions on design automation of electronic systems 2024-02, Vol.29 (2), p.1-29, Article 31
Hauptverfasser: S., Deepanjali, Mahammad SK, Noor
Format: Artikel
Sprache:eng
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Zusammenfassung:Controllers are mission-critical components of any electronic design. By sending control signals, they decide which and when other data path elements must operate. Faults, especially Single Event Upset (SEU) occurrence in these components, can lead to functional/mission failure of the system when deployed in harsh environments. Hence, competence to self-heal from SEU is highly required in the control path of the digital system. Reconfiguration is critical for recovering from a faulty state to a non-faulty state. Compared to native reconfiguration, the Virtual Reconfigurable Circuit (VRC) is an FPGA-generic reconfiguration mechanism. The non-partial reconfiguration in VRC and extensive architecture are considered hindrances in extending the VRC-based Evolvable Hardware (EHW) to real-time fault mitigation. To confront this challenge, we have proposed an intrinsic constrained evolution to improve the scalability and accelerate the evolution process for VRC-based fault mitigation in mission-critical applications. Experimentation is conducted on complex ACM/SIGDA benchmark circuits and real-time circuits used in space missions, which are not included in related works. In addition, a comparative study is made between existing and proposed methodologies for brushless DC motor control circuits. The hardware utilization in the multiplexer has been significantly reduced, resulting in up to a 77% reduction in the existing VRC architecture. The proposed methodology employs a fault localization approach to narrow the search space effectively. This approach has yielded an 87% improvement on average in convergence speed, as measured by the evolution time, compared to the existing work.
ISSN:1084-4309
1557-7309
DOI:10.1145/3634682