Dependable DNN Accelerator for Safety-Critical Systems: A Review on the Aging Perspective

Abstract

Nowadays, artificial intelligence (AI) and deep learning (DL) progressively adapt to various spheres of our lives. These disciplines contain safety-critical applications such as autonomous driving with a high risk of human injury in the case of malfunction, requiring a high promise of dependability. Even the dependability becomes more crucial as shrinking CMOS technology feature size worsens the resilience concerns due to factors like aging. This paper addresses the overarching dependability issue of advanced deep neural networks (DNN) accelerators from the aging perspective. Especially, a comprehensive survey and taxonomy of techniques used to evaluate and mitigate aging effects are introduced. We cover different aging effects like permanent faults, timing errors, and lifetime issues. We review research by the layer-wise approach and categorize several resilience classes to bring out major features. The concluding part of this review highlights the questions answered and several future research directions. This study is expected to benefit researchers in different areas of DNN deployment, especially the dependability of this emergent paradigm.

Highly Fault-Tolerant Systolic-Array-Based Matrix Multiplication

“…It should be noted that our objective is to ensure the accuracy of matrix multiplication results. Therefore, the fault-tolerance issues that we address in matrix multiplication are different from those in the literature on DNN accelerators [31][32][33][34], which may tolerate some errors.…”

“…Note that, in recent years, there have also been some studies [31][32][33][34] exploring faulttolerant designs for systolic-array-based deep neural network (DNN) accelerators. Due to the inherent fault-tolerant nature of artificial intelligence applications, these studies' faulttolerant designs may allow for some errors.…”

“…Due to the inherent fault-tolerant nature of artificial intelligence applications, these studies' faulttolerant designs may allow for some errors. Unlike these studies [31][32][33][34], we must ensure the correctness of the matrix multiplication results. Therefore, the fault-tolerance issues that we investigate in matrix multiplication differ fundamentally from those addressed in the literature on the fault tolerance of DNN accelerators.…”

Analysis and Enhancement of Resilience for LSTM Accelerators Using Residue-Based CEDs

“…For efficient performance of LSTMs in the inference phase, dedicated hardware accelerators are employed [2], [6], [7]. However, these accelerators are susceptible to faults stemming from fabrication technology and hardware architectures [8], [9], [10], [11], [12]. From the fabrication technology perspective, in the resilience-wall era [13], faults are caused by factors such as high-energy cosmic radiation, aging, and temperature variations [12].…”

“…However, these accelerators are susceptible to faults stemming from fabrication technology and hardware architectures [8], [9], [10], [11], [12]. From the fabrication technology perspective, in the resilience-wall era [13], faults are caused by factors such as high-energy cosmic radiation, aging, and temperature variations [12]. Those factors that are part of the physics of hardware are inevitable in hardware systems.…”