{"ID":5675075,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-05T01:57:11.175896696Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01602","arxiv_id":"2607.01602","title":"ProWAFT: A ROMA-LPD Instance for Workload-Aware and Dynamic Fault Tolerance in FPGA-Based CNN Accelerators","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) improves correctness but incurs substantial performance and energy overhead, while reactive recovery may introduce unacceptable latency on the critical path. We propose \\textbf{ProWAFT}, a proactive workload-aware fault-tolerance framework for FPGA-based CNN accelerators that uses partial reconfiguration to selectively apply TMR across reconfigurable partitions. ProWAFT quantifies workload criticality, models fault propagation and reconfiguration overhead, and selects configurations that minimize a composite objective over latency, energy, and reliability risk. Implemented on a Xilinx Zynq UltraScale+ ZCU104 platform with six reconfigurable regions and evaluated on a 500-task trace derived from ResNet-18, MobileNetV2, and EfficientNet-Lite under time-varying SEU injection, ProWAFT achieves lower composite cost than static TMR and reactive reconfiguration while maintaining high task success rate and near-baseline throughput with low online decision overhead.","short_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) improves correctness but incurs substantial performance and energy overhead, while reac...","url_abs":"https://arxiv.org/abs/2607.01602","url_pdf":"https://arxiv.org/pdf/2607.01602v1","authors":"[\"Xinxin Chen\",\"Haoran Qiao\",\"Yiming Guo\",\"Kecheng Luo\",\"Siyuan Feng\",\"Jingwen Ma\"]","published":"2026-07-02T02:04:07Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}