Aspis: Lightweight Neural Network Protection Against Soft Errors
Anna Schmedding, Lishan Yang, Adwait Jog, Evgenia Smirni · 2024
Convolutional neural networks (CNN) are incorporated into many image-based tasks across a variety of domains. Some of these are safety critical tasks such as object classification/detection and lane detection for self-driving cars. These applications have strict safety requirements and must guarantee the reliable operation of the neural networks in the presence of soft errors (i.e., transient faults) in DRAM. Standard safety mechanisms (e.g., triplication of data/computation) provide high resilience, but introduce intolerable overhead. We perform detailed characterization and propose an efficient methodology for pinpointing critical weights by using an efficient proxy, the Taylor criterion. Using this characterization, we design Aspis, an efficient software protection scheme that does selective weight hardening and offers a performance/reliability tradeoff. Aspis provides higher resilience comparing to state-of-the-art methods and is integrated into PyTorch as a fully-automated library.