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arXiv · 2401.12350

Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge

Yao Lu·Hiram Rayo Torres Rodriguez·Sebastian Vogel·Nick van de Waterlaat·Pavol Jancura
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Abstract

Neural Architecture Search (NAS) has become the de-facto approach for designing accurate and efficient networks for edge devices. Since models are typically quantized for edge deployment, recent work has investigated quantization-aware NAS (QA-NAS) to search for highly accurate and efficient quantized models. However, existing QA-NAS approaches, particularly few-bit mixed-precision (FB-MP) methods, do not scale to larger tasks. Consequently, QA-NAS has mostly been limited to low-scale tasks and tiny networks. In this work, we present an approach to enable QA-NAS (INT8 and FB-MP) on large-scale tasks by leveraging the block-wise formulation introduced by block-wise NAS. We demonstrate strong results for the semantic segmentation task on the Cityscapes dataset, finding FB-MP models 33% smaller and INT8 models 17.6% faster than DeepLabV3 (INT8) without compromising task performance.

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Yao Lu, Hiram Rayo Torres Rodriguez, Sebastian Vogel, Nick van de Waterlaat, Pavol Jancura. 2024-01-22. Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge. https://doi.org/10.1145/3615338.3618122

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