Trouble-Shooting at GAN Point: Improving Functional Safety in Deep Learning Accelerators

Kundu, Shamik; Banerjee, Suvadeep; Raha, Arnab; Su, Fei; Natarajan, Suriyaprakash; Basu, Kanad

doi:10.1109/tc.2023.3241218

Cited by 4 publications

(1 citation statement)

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“…The state-of-the-art research [77] proposes an approach to improve functional safety (FuSa) in DNN accelerators for mission-critical applications using the concept of generative adversarial networks (GANs). The proposed method produces a set of functional test patterns based on GAN that are independent of the DNN model and the accelerator features.…”

Section: A Model-level Resiliencementioning

confidence: 99%

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

Moghaddasi,

Gorgin,

Lee

2023

IEEE Access

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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.

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Section: A Model-level Resiliencementioning

confidence: 99%