Fault Resilience of DNN Accelerators for Compressed Sensor Inputs
Ayush Arunachalam, Shamik Kundu, Arnab Raha, Suvadeep Banerjee, Kanad Basu · 2022
The sensor subsystem is a crucial component in a Deep Neural Network (DNN) inference framework. However, the high amount of sensor data being generated manifests as an energy bottleneck in resource-constrained edge devices. Towards this end, we propose SeNNse, a novel sensor compression methodology that optimizes the energy requirement of sensor subsystems, which involves a two-step approach of subsampling and subsequent supersampling of the sensor images via inter-polation. However, such compressed sensor inputs may result in substantial performance degradation in the presence of bit-flip faults manifested in DNN accelerators (as shown in this paper). Such faults occur frequently in semiconductor device memory due to variegated reasons, ranging from impingement of high-energy particles to structural deformities. We evaluate our approach on Multilayer Perceptrons (MLP), trained on MNIST, EMNIST, and CIFAR-10 datasets. Our proposed SeNNse frame-work furnishes maximum energy savings of 62.1%, with a negligible reduction in classification accuracy. However, our results also indicate larger performance degradation, of up to 21.56%, due to bit-flip faults for such compressed inputs, which is mainly attributed to the concise set of input activations being fed to the neural networks during inference.