Recovery algorithm to correct silent data corruption of synaptic storage in convolutional neural networks

Article type: Research Article

Authors: Roy, Arighna^* | Ludwig, Simone A.

Affiliations: Department of Computer Science, North Dakota State University, Fargo, ND, USA

Correspondence: [*] Corresponding author: Arighna Roy, Department of Computer Science, North Dakota State University, Fargo, ND, USA. E-mail: [email protected].

Abstract: With the surge of computational power and efficient energy consumption management on embedded devices, embedded processing has grown exponentially during the last decade. In particular, computer vision has become prevalent in real-time embedded systems, which have always been a victim of transient fault due to its pervasive presence in harsh environments. Convolutional Neural Networks (CNN) are popular in the domain of embedded vision (computer vision in embedded systems) given the success they have shown. One problem encountered is that a pre-trained CNN on embedded devices is vastly affected by Silent Data Corruption (SDC). SDC refers to undetected data corruption that causes errors in data without any indication that the data is incorrect, and thus goes undetected. In this paper, we propose a software-based approach to recover the corrupted bits of a pre-trained CNN due to SDC. Our approach uses a rule-mining algorithm and we conduct experiments on the propagation of error through the topology of the CNN in order to detect the association of the bits for the weights of the pre-trained CNN. This approach increases the robustness of safety-critical embedded vision applications in volatile conditions. A proof of concept has been conducted for a combination of a CNN and a vision data set. We have successfully established the effectiveness of this approach for a very high level of SDC. The proposed approach can further be extended to other networks and data sets.

Keywords: Silent data corruption, CNN, AlexNet, association rule mining

DOI: 10.3233/HIS-200278

Journal: International Journal of Hybrid Intelligent Systems, vol. 16, no. 3, pp. 177-187, 2020