用统计特征动态修复近似神经网络中的故障，兼顾效率与准确率。
EPSILON: Adaptive Fault Mitigation in Approximate Deep Neural Network using Statistical Signatures
- 通过预计算的统计签名和分层重要性评估，实现无需中断的快速故障检测。
- 在多种硬件故障场景下保持80.05%模型准确率，推理速度提升22%、能效提高28%。
- 适合对可靠性要求高且资源受限的边缘安全应用，如智能设备与自动驾驶。
近似计算在深度神经网络加速器（AxDNN）中的应用可显著提升能效。然而，永久性故障会严重降低AxDNN性能。传统故障检测与修复方法虽适用于精确网络（AccDNN），但开销大、延迟高，难以用于资源受限的实时部署。为此，本文提出EPSILON——一种轻量级框架，利用预计算的统计签名与分层重要性度量，在AxDNN中实现高效故障检测与修复。该框架引入一种新型非参数化模式匹配算法，可在不中断执行的情况下实现常数时间故障检测，并动态适配不同网络结构与故障模式。通过统计权重分布与层重要性分析，智能调整修复策略，在保持近似计算能效优势的同时维持模型精度。在多种近似乘法器、AxDNN架构、主流数据集（MNIST、CIFAR-10、CIFAR-100、ImageNet-1k）及故障场景下的广泛评测表明，EPSILON在保持80.05%准确率的前提下，实现推理时间提升22%、能效提高28%，为安全关键型边缘应用提供了实用可靠的AxDNN部署方案。
原文摘要 · Abstract (English)
The increasing adoption of approximate computing in deep neural network accelerators (AxDNNs) promises significant energy efficiency gains. However, permanent faults in AxDNNs can severely degrade their performance compared to their accurate counterparts (AccDNNs). Traditional fault detection and mitigation approaches, while effective for AccDNNs, introduce substantial overhead and latency, making them impractical for energy-constrained real-time deployment. To address this, we introduce EPSILON, a lightweight framework that leverages pre-computed statistical signatures and layer-wise importance metrics for efficient fault detection and mitigation in AxDNNs. Our framework introduces a novel non-parametric pattern-matching algorithm that enables constant-time fault detection without interrupting normal execution while dynamically adapting to different network architectures and fault patterns. EPSILON maintains model accuracy by intelligently adjusting mitigation strategies based on a statistical analysis of weight distribution and layer criticality while preserving the energy benefits of approximate computing. Extensive evaluations across various approximate multipliers, AxDNN architectures, popular datasets (MNIST, CIFAR-10, CIFAR-100, ImageNet-1k), and fault scenarios demonstrate that EPSILON maintains 80.05\% accuracy while offering 22\% improvement in inference time and 28\% improvement in energy efficiency, establishing EPSILON as a practical solution for deploying reliable AxDNNs in safety-critical edge applications.
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