机器学习测试机器学习硬件:一个良性循环

Arjun Chaudhuri, Jonti Talukdar, K. Chakrabarty
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引用次数: 0

摘要

深度神经网络(DNN)的广泛应用导致了对人工智能加速器的需求上升。dnn特定的功能临界性分析识别导致可接受要求(如推理精度)的可测量和重大偏差的故障。本文研究了收缩阵列加速器处理单元中结构故障的分类问题。我们首先提出了一种基于双层机器学习(ML)的方法来评估故障的功能临界性。虽然监督学习技术可以用来准确地估计故障的严重性,但它需要大量的基础真值来进行模型训练。因此,我们描述了一个神经-孪生框架,用于用可忽略不计的真实数据分析故障临界性。我们进一步描述了一个拓扑和概率框架来估计在存在缺陷的情况下PE的主输出(POs)翻转的预期数量,并使用PO-flip计数作为确定故障临界性的替代。我们证明了PO-flip计数和内部网络的神经孪生敏感性分析的组合可以用作现有的基于ml的临界分类器的附加特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning for Testing Machine-Learning Hardware: A Virtuous Cycle∗
The ubiquitous application of deep neural networks (DNN) has led to a rise in demand for AI accelerators. DNN-specific functional criticality analysis identifies faults that cause measurable and significant deviations from acceptable requirements such as the inferencing accuracy. This paper examines the problem of classifying structural faults in the processing elements (PEs) of systolic-array accelerators. We first present a two-tier machine-learning (ML) based method to assess the functional criticality of faults. While supervised learning techniques can be used to accurately estimate fault criticality, it requires a considerable amount of ground truth for model training. We therefore describe a neural-twin framework for analyzing fault criticality with a negligible amount of ground-truth data. We further describe a topological and probabilistic framework to estimate the expected number of PE’s primary outputs (POs) flipping in the presence of defects and use the PO-flip count as a surrogate for determining fault criticality. We demonstrate that the combination of PO-flip count and neural twin-enabled sensitivity analysis of internal nets can be used as additional features in existing ML-based criticality classifiers.
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