Special Session: Approximation and Fault Resiliency of DNN Accelerators
Mohammad Hasan Ahmadilivani Mario Barbareschi Salvatore Barone Alberto Bosio Masoud Daneshtalab Salvatore Della Torca Gabriele Gavarini Maksim Jenihhin Jaan Raik Annachiara Ruospo Ernesto Sanchez Mahdi Taheri
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