QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise

• URL: http://arxiv.org/abs/2208.14839v4
• Date: Wed, 10 Jan 2024 15:52:03 GMT
• Title: QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise
• Authors: Egor Shvetsov, Dmitry Osin, Alexey Zaytsev, Ivan Koryakovskiy, Valentin Buchnev, Ilya Trofimov, Evgeny Burnaev
• Abstract summary: We propose a novel quantization-aware procedure, the QuantNAS. We use entropy regularization, quantization noise, and Adaptive Deviation for Quantization (ADQ) module to enhance the search procedure. The proposed procedure is 30% faster than direct weight quantization and is more stable.
• Score: 19.897685398009912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is a constant need for high-performing and computationally efficient neural network models for image super-resolution: computationally efficient models can be used via low-capacity devices and reduce carbon footprints. One way to obtain such models is to compress models, e.g. quantization. Another way is a neural architecture search that automatically discovers new, more efficient solutions. We propose a novel quantization-aware procedure, the QuantNAS that combines pros of these two approaches. To make QuantNAS work, the procedure looks for quantization-friendly super-resolution models. The approach utilizes entropy regularization, quantization noise, and Adaptive Deviation for Quantization (ADQ) module to enhance the search procedure. The entropy regularization technique prioritizes a single operation within each block of the search space. Adding quantization noise to parameters and activations approximates model degradation after quantization, resulting in a more quantization-friendly architectures. ADQ helps to alleviate problems caused by Batch Norm blocks in super-resolution models. Our experimental results show that the proposed approximations are better for search procedure than direct model quantization. QuantNAS discovers architectures with better PSNR/BitOps trade-off than uniform or mixed precision quantization of fixed architectures. We showcase the effectiveness of our method through its application to two search spaces inspired by the state-of-the-art SR models and RFDN. Thus, anyone can design a proper search space based on an existing architecture and apply our method to obtain better quality and efficiency. The proposed procedure is 30\% faster than direct weight quantization and is more stable.

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