Abstract

As AI is increasingly adopted in industry, ensuring reliability is becoming even more critical. AI systems are utilized across a variety of fields, and high reliability is essential, particularly in areas where human lives are at stake, such as self-driving and medical diagnostics. Additionally, devices that operate in space environments, such as satellites, must possess strong error resistance against cosmic ray interference. In this tutorial, we will first explore the reliability requirements for future AI applications. Next, focusing on error tolerance as a key measure of reliability, we will explain the effects of deep learning model optimization techniques, such as quantization and pruning, and their impact on error tolerance. We will also examine the evolution of deep learning accelerators and their role in enhancing error tolerance. Finally, we will discuss future challenges and prospects for ensuring the reliability of AI systems.

Bio

He received the B.E., M.E., and D.E. degrees from the Tokyo Institute of Technology, Tokyo, Japan, in 2005, 2006, and 2009, respectively. He was a Research Associate with the Tokyo Institute of Technology, until 2009. He was an Assistant Professor with the Division of Advanced Electrical and Electronics Engineering, Tokyo University of Agriculture and Technology, until 2015. He was an Associate Professor with the School of Computer Science and Engineering, The University of Aizu, until 2018, where he has been a Senior Associate Professor, since 2019. His research interests include image processing, hardware acceleration, efficient and fault-tolerant AI systems, electrical design automation, and combinational algorithms.