Image Test Libraries for the In-Field Test of Ultra-Low-Power Devices

In recent years, research and technology advancements have driven exponential growth in the adoption of Artificial Intelligence (AI)-based systems, even in safety-critical contexts such as autonomous driving and healthcare applications. The joint effort of academia and industry has yielded techniques and standards with the objective of ensuring the safe operation of AI-based technology. In the specific context of Convolutional Neural Networks (CNNs) running on GPUs, Image Test Libraries (ITLs) have been proposed as an effective method for performing on-line functional testing of GPU multipliers. This is achieved by launching the inference of a set of test images containing a set of ATPG-generated functional test patterns. However, while the demand for computational power for DNN models is constantly increasing, another branch of Machine Learning (ML) research, namely TinyML, focuses on minimizing the computational requirements of DNN models in order to bring AI capabilities to edge devices, whose constraints on power usage, memory space and processing power do not allow for the deployment of conventional DNN models. This research work aims to adapt the ITL technique to CNNs running on ultra-low-power edge hardware, while also overcoming some limitations of GPU ITLs. Experimental results demonstrate that a single test image generated using the proposed method is capable of detecting 96.01% of stuck-at faults occurring in the 32-bit integer multiplier of a RISC-V-based ultra-low-power System-on-Chip executing a quantized CNN.