研究人员开发了ProWAFT，一个专为SRAM基FPGA上实现的CNN加速器设计的新型容错框架。该系统解决了瞬态故障带来的挑战，这些故障可能损害边缘计算环境的可靠性。ProWAFT利用部分重构，跨可重构分区动态应用三模冗余（TMR），平衡工作负载关键性、故障传播和重构开销，以优化延迟、能耗和可靠性。 AI
影响 增强了网络边缘AI推理硬件的可靠性，这对于实时应用至关重要。
排序理由 该条目是一篇学术论文，详细介绍了硬件加速器的新技术框架。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv:2607.01602v1 Announce Type: new Abstract: SRAM-based FPGAs provide an attractive platform for energy- and latency-constrained CNN inference at the network edge, yet transient faults can lead to silent errors that compromise reliability. Always-on redundancy (e.g., full TMR)…
SRAM-based FPGAs provide an attractive platform for energy- and latency-constrained CNN inference at the network edge, yet transient faults can lead to silent errors that compromise reliability. Always-on redundancy (e.g., full TMR) improves correctness but incurs substantial per…