神经网络处理的自适应软错误防护
Adaptive Soft Error Protection for Neural Network Processing
AI总结：
本文提出基于动态输入依赖性的自适应软错误防护框架，利用轻量级图神经网络预测神经网络组件的软错误脆弱性，降低计算开销并提升保护效率。
摘要：
先前针对神经网络组件的选择性保护研究通常仅利用静态漏洞差异。尽管这些方法改进了经典模块冗余，但仍然对内存密集型和计算密集型神经网络工作负载造成显著开销。在本工作中，我们观察到神经网络的脆弱性也依赖于输入并在运行时动态变化。基于这一观察，我们提出了一种自适应、漏洞感知的容错框架。其核心是一个轻量级图神经网络（GNN）模型，能够动态预测输入和神经网络组件的软错误脆弱性，从而实现容错策略的实时适应。这种设计相比传统方法提供了互补且更高效的保护方案。实验结果表明，GNN预测器在识别关键输入和组件方面实现了超过95%的准确性。此外，我们的自适应方案将计算开销平均降低了42.12%，同时保持模型精度，显著优于静态选择性保护方法。
英文摘要：
Previous research on selective protection for neural network components typically exploits only static vulnerability differences. Although these methods improve upon classical modular redundancy, they still incur substantial overhead for neural network workloads that are both memory-intensive and compute-intensive. In this work, we observe that neural network vulnerability is also input-dependent and varies dynamically at runtime. With this observation, we propose an adaptive, vulnerability-aware fault tolerance framework. At its core, a lightweight graph neural network (GNN) model dynamically predicts soft error vulnerabilities across inputs and neural network components, enabling real-time adaptation of fault tolerance policies. This design offers a complementary and more efficient protection scheme compared to traditional approaches. Experimental results demonstrate that the GNN predictor achieves over 95% accuracy in identifying critical inputs and components. Moreover, our adaptive scheme reduces computational overhead by an average of 42.12% while preserving model accuracy, significantly outperforming static selective protection methods.
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。
