A forward error compensation approach for fault resilient deep neural network accelerator design

• Main Authors: Liu, Wenye, Chang, Chip Hong
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering::Computer hardware, software and systems; Hardware Security; Fault Injection Attack; Deep Neural Network Accelerator
• Online Access: https://hdl.handle.net/10356/155879
• Institution: Nanyang Technological University

Deep learning accelerator is a key enabler of a variety of safety-critical applications such as self-driving car and video surveillance. However, recently reported hardware-oriented attack vectors, e.g., fault injection attacks, have extended the threats on deployed deep neural network (DNN) systems...