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For real-time computing systems, energy efficiency, Quality of Service, and fault tolerance are among the major design concerns. In this work, we study the problem of reliable and energy-aware fixed-priority (m,k)-deadlines enforcement with standby-sparing. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. In order to reduce energy consumption for such kind of systems, we proposed a novel scheduling scheme under the QoS constraint of (m,k)- deadlines. The evaluation results demonstrate that our proposed approach significantly outperformed the previous research in energy conservation while assuring (m,k)-deadlines and fault tolerance for real-time systems.
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This content will become publicly available on June 29, 2026
EdgeGuard: Robust and Fault-Aware Design for Resilient Edge Computing AI Accelerators
Smaller transistor feature sizes have made integrated circuits (ICs) more vulnerable to permanent faults. This leads to short lifetimes and increased risk of faults that lead to catastrophic errors. Fortunately, Artificial Neural Networks (ANNs) are error resilient as their accuracies can be maintained through e.g., fault-aware re-training. One of the problems though with previous work is that they require a re-design in the individual neuron processing element structure in order to efficiently deal with these faults. In this work, we propose a novel architecture combined with a design flow that performs a fault-aware weight re-assignment in order to minimize the effect of permanent faults on the accuracy of ANNs mapped to AI accelerator without the need of time-consuming fault-aware re-training nor neuron processing elements re-design. In particular, we deal with Tensor Processing Units (TPUs) although our proposed approach is also extensible to any other architecture. Experimental results show that our proposed approach and can be efficiently executed on a fast dedicated hardware re-binding unit or on software.
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- Award ID(s):
- 2323819
- PAR ID:
- 10618287
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400714962
- Page Range / eLocation ID:
- 134 to 140
- Format(s):
- Medium: X
- Location:
- New Orleans LA USA
- Sponsoring Org:
- National Science Foundation
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