Regularization-based Framework for Quantization-, Fault- and Variability-Aware Training
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916839690338304 |
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| author | Biswas, Anmol Singhal, Raghav Elangovan, Sivakumar Sabnis, Shreyas Ganguly, Udayan |
| author_facet | Biswas, Anmol Singhal, Raghav Elangovan, Sivakumar Sabnis, Shreyas Ganguly, Udayan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_01297 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Regularization-based Framework for Quantization-, Fault- and Variability-Aware Training Biswas, Anmol Singhal, Raghav Elangovan, Sivakumar Sabnis, Shreyas Ganguly, Udayan Machine Learning 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. |
| title | Regularization-based Framework for Quantization-, Fault- and Variability-Aware Training |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.01297 |