2022/04/05 by Cheng Liu, Zhen Gao, Liu, Cheng +9
Engineering · #Advanced Memory and Neural Computing #Artificial Intelligence (cs.AI) #B.2.3 #B.8.1 #FOS: Computer and information sciences #Ferroelectric and Negative Capacitance Devices #Hardware Architecture (cs.AR) #Machine Learning (cs.LG) #Radiation Effects in Electronics
paper · pdf · doi:10.48550/arxiv.2204.01942
openalex publication_date 2022/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the rapid advancements of deep learning in the past decade, it can be foreseen that deep learning will be continuously deployed in more and more safety-critical applications such as autonomous driving and robotics. In this context, reliability turns out to be critical to the deployment of deep learning in these applications and gradually becomes a first-class citizen among the major design metrics like performance and energy efficiency. Nevertheless, the back-box deep learning models combined with the diverse underlying hardware faults make resilient deep learning extremely challenging. In this special session, we conduct a comprehensive survey of fault-tolerant deep learning design approaches with a hierarchical perspective and investigate these approaches from model layer, architecture layer, circuit layer, and cross layer respectively.