Special Session: Approximation and Fault Resiliency of DNN Accelerators

• URL: http://arxiv.org/abs/2306.04645v1
• Date: Wed, 31 May 2023 19:27:45 GMT
• Title: Special Session: Approximation and Fault Resiliency of DNN Accelerators
• Authors: Mohammad Hasan Ahmadilivani, Mario Barbareschi, Salvatore Barone, Alberto Bosio, Masoud Daneshtalab, Salvatore Della Torca, Gabriele Gavarini, Maksim Jenihhin, Jaan Raik, Annachiara Ruospo, Ernesto Sanchez, and Mahdi Taheri
• Abstract summary: This paper explores the approximation and fault resiliency of Deep Neural Network accelerators. We propose to use approximate (AxC) arithmetic circuits to emulate errors in hardware without performing fault injection on the DNN. We also propose a fine-grain analysis of fault resiliency by examining fault propagation and masking in networks.
• Score: 0.9126382223122612
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in many scenarios, including safety-critical applications such as autonomous driving. In this context, besides energy efficiency and performance, reliability plays a crucial role since a system failure can jeopardize human life. As with any other device, the reliability of hardware architectures running DNNs has to be evaluated, usually through costly fault injection campaigns. This paper explores the approximation and fault resiliency of DNN accelerators. We propose to use approximate (AxC) arithmetic circuits to agilely emulate errors in hardware without performing fault injection on the DNN. To allow fast evaluation of AxC DNN, we developed an efficient GPU-based simulation framework. Further, we propose a fine-grain analysis of fault resiliency by examining fault propagation and masking in networks

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