Investigating the Impact of Radiation-Induced Soft Errors on the Reliability of Approximate Computing Systems

2020 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT) Pub Date : 2020-10-19 DOI:10.1109/DFT50435.2020.9250865

Lucas Matana Luza, D. Söderström, G. Tsiligiannis, H. Puchner, C. Cazzaniga, Ernesto Sánchez, A. Bosio, L. Dilillo

引用次数: 13

Abstract

Approximate Computing (AxC) is a well-known paradigm able to reduce the computational and power overheads of a multitude of applications, at the cost of a decreased accuracy. Convolutional Neural Networks (CNNs) have proven to be particularly suited for AxC because of their inherent resilience to errors. However, the implementation of AxC techniques may affect the intrinsic resilience of the application to errors induced by Single Events in a harsh environment. This work introduces an experimental study of the impact of neutron irradiation on approximate computing techniques applied on the data representation of a CNN.

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研究辐射软误差对近似计算系统可靠性的影响

近似计算(AxC)是一种众所周知的范例，能够以降低准确性为代价，减少大量应用程序的计算和功耗开销。卷积神经网络(cnn)已被证明特别适合于AxC，因为它们对错误的固有弹性。然而，AxC技术的实现可能会影响应用程序对恶劣环境中单个事件引起的错误的内在弹性。本文介绍了中子辐照对近似计算技术影响的实验研究，该技术应用于CNN的数据表示。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2020 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)

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