Security of neuromorphic computing: Thwarting learning attacks using memristor's obsolescence effect

Conference Paper

© 2016 ACM. Neuromorphic architectures are widely used in many applications for advanced data processing, and often implements proprietary algorithms. In this work, we prevent an attacker with physical access from learning the proprietary algorithm implemented by the neuromorphic hardware. For this purpose, we leverage the obsolescence effect in memristors to judiciously reduce the accuracy of outputs for any unauthorized user. For a legitimate user, we regulate the obsolescence effect, thereby controlling the accuracy of outputs. We also analyze the security vs. cost trade-offs for different applications. Our methodology is compatible with mainstream classification applications, memristor devices, and security and performance constraints.

Full Text

Duke Authors

Cited Authors

  • Yang, C; Liu, B; Li, H; Chen, Y; Wen, W; Barnell, M; Wu, Q; Rajendran, J

Published Date

  • November 7, 2016

Published In

Volume / Issue

  • 07-10-November-2016 /

International Standard Serial Number (ISSN)

  • 1092-3152

International Standard Book Number 13 (ISBN-13)

  • 9781450344661

Digital Object Identifier (DOI)

  • 10.1145/2966986.2967074

Citation Source

  • Scopus