Concurrent Fault Detection for Binary Neural Network Accelerators via On-Chip Voltage Monitoring

As Neural Networks (NNs) are increasingly deployed in safety-critical edge and datacenter systems, ensuring reliable execution becomes essential. Runtime faults such as memory bit flips and faults in logic components can silently corrupt computations without triggering system-level alarms. Conventional detection methods often miss logic faults or incur significant overhead. We propose a lightweight, concurrent error detection method that monitors voltage fluctuation traces captured by on-chip sensors. Our hypothesis is that faults alter neuron activations and change the switching activity and thus the instantaneous voltage fluctuation profile during inference. These traces are classified using a threshold-based model, requiring no modifications to the NN hardware or inference pipeline. As our approach operates purely through side-channel observation, it functions as a non-intrusive wrapper applicable to a wide range of AI accelerators. We evaluate the method on two different FPGAs, demonstrating consistent efficiency across platforms and portability to cloud scenarios. It detects faults in under a second, making it suitable for real-time applications such as vision tasks running at 30–60 FPS. ... mehrBy repurposing voltage sensors as diagnostic tools, this work opens a new direction for functional safety in AI hardware.