一句话：提出 PrioriFI，利用 Hessian 初始排序与历史故障注入启发式，优先定位边缘神经网络中最敏感比特的故障注入算法。
边缘神经网络在功耗、延迟、内存等严格约束下运行，同时必须对硬件故障保持鲁棒。传统故障注入评估成本高、效率低，难以快速定位对网络正确性最关键的比特。PrioriFI 是一种更有依据的故障注入算法：它首先利用 Hessian 对神经网络比特进行初始故障敏感性排序，然后在故障注入过程中，将已注入得到的信息作为启发式，引导后续注入优先 targeting 最可能敏感的下一个比特。通过这种优先级策略，PrioriFI 能更快发现高故障敏感比特，帮助设计者快速评估不同神经网络，并更好地协同设计容错边缘神经网络系统。
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 a more informed fault injection (FI) algorithm that evaluates edge NN robustness by ranking NN bits based on their fault sensitivity. PrioriFI prioritizes finding highly fault-sensitive bits, that is, the bits most critical to an NN's correctness, first. To accomplish this, PrioriFI uses the Hessian for the initial parameter ranking. Then, during an FI campaign, PrioriFI uses the information gained from past FIs as a heuristic so that future FIs target the bits likely to be the next most sensitive. With PrioriFI, designers can quickly evaluate different NNs and better co-design fault-tolerant edge NN systems.