2021/12/05 by Jashanpreet Singh Sraw, Sraw, Jashanpreet Singh, M C Deepak +1
Computer Science · Engineering · #Advanced Neural Network Applications #FOS: Computer and information sciences #Fault Detection and Control Systems #Industrial Vision Systems and Defect Detection #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2112.02657
openalex publication_date 2021/12/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Today, Neural Networks are the basis of breakthroughs in virtually every technical domain. Their application to accelerators has recently resulted in better performance and efficiency in these systems. At the same time, the increasing hardware failures due to the latest (shrinked) semiconductor technology needs to be addressed. Since accelerator systems are often used to back time-critical applications such as self-driving cars or medical diagnosis applications, these hardware failures must be eliminated. Our research evaluates these failures from a systemic point of view. Based on our results, we find critical results for the system reliability enhancement and we further put forth an efficient method to avoid these failures with minimal hardware overhead.