Regularization-based Framework for Quantization-, Fault- and Variability-Aware Training
This work addresses efficiency and reliability challenges for deploying deep learning models on low-power edge devices, representing an incremental improvement by extending quantization-aware training to handle faults and variability.
The paper tackles the problem of non-ideal hardware behavior like bit faults and variability in low-bit quantization for edge AI devices, proposing a regularization-based training framework that achieves competitive results on datasets like CIFAR-10 and ImageNet, and significantly improves performance recovery under up to 20% bit-fault rate and 40% device-to-device variability.
Efficient inference is critical for deploying deep learning models on edge AI devices. Low-bit quantization (e.g., 3- and 4-bit) with fixed-point arithmetic improves efficiency, while low-power memory technologies like analog nonvolatile memory enable further gains. However, these methods introduce non-ideal hardware behavior, including bit faults and device-to-device variability. We propose a regularization-based quantization-aware training (QAT) framework that supports fixed, learnable step-size, and learnable non-uniform quantization, achieving competitive results on CIFAR-10 and ImageNet. Our method also extends to Spiking Neural Networks (SNNs), demonstrating strong performance on 4-bit networks on CIFAR10-DVS and N-Caltech 101. Beyond quantization, our framework enables fault and variability-aware fine-tuning, mitigating stuck-at faults (fixed weight bits) and device resistance variability. Compared to prior fault-aware training, our approach significantly improves performance recovery under upto 20% bit-fault rate and 40% device-to-device variability. Our results establish a generalizable framework for quantization and robustness-aware training, enhancing efficiency and reliability in low-power, non-ideal hardware.