On-line Functional Testing of Memristor-mapped Deep Neural Networks using Backdoored Checksums

Deep learning (DL) applications are becoming increasingly ubiquitous. However, recent research has highlighted a number of reliability concerns associated with deep neural networks (DNNs) used for DL. In particular, hardware-level reliability of DNNs is of concern when DL models are mapped to specialized neuromorphic hardware such as memristor-based crossbars. Faults in the crossbars can deviate the corresponding DNN model weights from their trained values. It is therefore desirable to have an on-device "checksum" function to indicate if model weights are deviated. We present a backdooring technique that fine-tunes DNN weights to implement the checksum function. The backdoored checksum function is triggered only when inferencing is carried out using a special set of data points with watermarks. We show that backdooring, i.e., fine-tuning of DNN weights, has no impact on the inferencing accuracy of the original DNN model. Moreover, the implemented checksum functions for AlexNet and VGG-16 remarkably outperform baseline approaches. Based on the proposed on-line functional testing solution, we present a computing framework that can efficiently recover the inferencing accuracy of a memristor-mapped DNN from weight deviations. Compared to related recent work, the proposed framework achieves 5.6x speed-up in time-to-recovery and reduces the on-chip test data volume by 99.99%.