ENFOR-SA：端到端跨层瞬态故障注入器用于在脉冲阵列上高效准确的DNN可靠性评估
ENFOR-SA: End-to-end Cross-layer Transient Fault Injector for Efficient and Accurate DNN Reliability Assessment on Systolic Arrays
AI总结：
本文提出ENFOR-SA，一种端到端框架，用于在脉冲阵列上进行DNN瞬态故障分析。通过跨层模拟和仅在故障注入时使用RTL脉冲阵列组件，实现高精度故障注入，比传统方法快2个数量级。
摘要：
近年来，深度学习的进步产生了高度准确但日益庞大复杂的DNN，使传统故障注入技术变得不切实际。准确的故障分析需要RTL准确的硬件模型。然而，这与软件-only方法相比显著减慢了评估速度，特别是当结合昂贵的HDL仪器时。在本文中，我们证明对于脉冲阵列（SA）架构，这种高开销的方法是不必要的，并提出ENFOR-SA，一种用于在SA上进行DNN瞬态故障分析的端到端框架。我们的两步方法采用跨层模拟，并仅在故障注入时使用RTL SA组件，其余部分在软件层面执行。在CNNs和Vision Transformers上的实验表明，ENFOR-SA仅比基于软件的注入慢6%的平均速度，同时在完整SoC RTL模拟中实现了至少两数量级的加速（平均569倍），并且比最先进的跨层RTL注入工具提高了2.03倍。ENFOR-SA的代码在https://github.com/rafaabt/ENFOR-SA上公开可用。
英文摘要：
Recent advances in deep learning have produced highly accurate but increasingly large and complex DNNs, making traditional fault-injection techniques impractical. Accurate fault analysis requires RTL-accurate hardware models. However, this significantly slows evaluation compared with software-only approaches, particularly when combined with expensive HDL instrumentation. In this work, we show that such high-overhead methods are unnecessary for systolic array (SA) architectures and propose ENFOR-SA, an end-to-end framework for DNN transient fault analysis on SAs. Our two-step approach employs cross-layer simulation and uses RTL SA components only during fault injection, with the rest executed at the software level. Experiments on CNNs and Vision Transformers demonstrate that ENFOR-SA achieves RTL-accurate fault injection with only 6% average slowdown compared to software-based injection, while delivering at least two orders of magnitude speedup (average $569\times$) over full-SoC RTL simulation and a $2.03\times$ improvement over a state-of-the-art cross-layer RTL injection tool. ENFOR-SA code is publicly available at https://github.com/rafaabt/ENFOR-SA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
