Out-of-Distribution Detection Using Power-Side Channels for Improving Functional Safety of Neural Network FPGA Accelerators

Accurate out-of-distribution (OOD) detection is crucial for ensuring the safety and reliability of neural network (NN) accelerators in real-world scenarios. This paper proposes a novel OOD detection approach for NN FPGA accelerators using remote power side-channel measurements. We assess different methods for distinguishing power measurements of in-distribution (ID) samples from OOD samples, comparing the effectiveness of simple power analysis and OOD sample identification based on the reconstruction error of an autoencoder (AE). Leveraging on-chip voltage sensors enables non-intrusive and concurrent remote OOD detection, eliminating the need for explicit labels or modifications to the underlying NN.