Reliability Exploration of Neural Network Accelerator
Thursday, December 05, 2024
Deep Neural Networks (DNNs) running on edge-computing devices are extending their applications to safety-critical areas like autonomous driving. Accordingly, the reliability of DNNs and their hardware platforms are garnering increased attention. This talk will focus on soft errors, predominantly caused by cosmic rays, a major error source during an intermediate device’s lifetime. While DNNs are inherently robust against bit flips, these errors can still lead to severe miscalculations due to weight and activation perturbations, bit flips in AI accelerators, and errors in their interfaces with microcontrollers, etc. This talk will discuss the identification of vulnerabilities in neural networks and reliability exploration of AI accelerators for edge computing.
Post Talk Link: Click Here
Passcode: =2f8TG6K
Speaker/s
Prof. Masanori Hashimoto received the B.E., M.E., and Ph.D. degrees in communications and computer engineering from Kyoto University, Kyoto, Japan, in 1997, 1999, and 2001, respectively. Now, he is a Professor in the Department of Communications and Computer Engineering, Kyoto University. His current research interests include VLSI design and CAD, especially design for reliability, soft error characterization, timing and power integrity analysis, reconfigurable computing, and low-power circuit design. He served as the TPC chair for ASP-DAC 2022 and MWSCAS 2022. He serves/served as the Editor-in-Chief for Microelectronics Reliability and an Associate Editor for IEEE Trans. VLSI Systems, IEEE Trans. CAS-I, and ACM Trans. Design Automation of Electronic Systems.
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