Speaker
Description
As neural networks (NNs) are increasingly used to provide
edge intelligence, there is a growing need to make the edge devices
that run them robust to faults. Edge devices must mitigate the resulting
hardware failures while maintaining strict constraints on power, energy,
latency, throughput, memory size, and computational resources. Edge
NNs require fundamental changes in model architecture, e.g., quantization and fewer, smaller layers. PrioriFI is an efficient fault injection (FI) algorithm that evaluates edge NN robustness by ranking NN bits based on their fault sensitivity. PrioriFI uses the Hessian for the initial parameter ranking. Then, during an FI campaign, PrioriFI uses the information gained from each FI to focus on the bits likely to be the next most sensitive. With PrioriFI, designers can quickly evaluate different NN architectures and co-design fault-tolerant edge NNs.