Special Session: Effective In-field Testing of Deep Neural Network Hardware Accelerators
Shamik Kundu, Suvadeep Banerjee, Arnab Raha, Kanad Basu · 2022
Ongoing research to obtain high performance Deep Neural Network (DNN) executions have led to the development of customized purpose-built deep learning inference accelerators. DNN accelerators are susceptible to faults, due to high-energy particles, process variations, temperature and structural deformities manifesting as latent defects. These faults can introduce misclassification, thereby jeopardizing the Functional Safety (FuSa) of the accelerator in mission mode, which can eventuate to disastrous consequences, including loss of human lives. In this paper, we explore the impact of such faults on the FuSa of a DNN accelerator by varying the network parameters, position and characteristics of the injected fault across multiple exhaustive datasets. Furthermore, we analyze the efficiency of a software-based self test scheme to detect FuSa violations in the accelerator in mission mode, that employs functional test patterns, akin to instances in the application dataset. The test patterns, selected from the dataset of the DNN, furnish up to 100% coverage with cardinality as low as 0.1% of the entire test dataset.