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arXiv · 2311.05317

RepQ: Generalizing Quantization-Aware Training for Re-Parametrized Architectures

Anastasiia Prutianova·Alexey Zaytsev·Chung-Kuei Lee·Fengyu Sun·Ivan Koryakovskiy
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Abstract

Existing neural networks are memory-consuming and computationally intensive, making deploying them challenging in resource-constrained environments. However, there are various methods to improve their efficiency. Two such methods are quantization, a well-known approach for network compression, and re-parametrization, an emerging technique designed to improve model performance. Although both techniques have been studied individually, there has been limited research on their simultaneous application. To address this gap, we propose a novel approach called RepQ, which applies quantization to re-parametrized networks. Our method is based on the insight that the test stage weights of an arbitrary re-parametrized layer can be presented as a differentiable function of trainable parameters. We enable quantization-aware training by applying quantization on top of this function. RepQ generalizes well to various re-parametrized models and outperforms the baseline method LSQ quantization scheme in all experiments.

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Anastasiia Prutianova, Alexey Zaytsev, Chung-Kuei Lee, Fengyu Sun, Ivan Koryakovskiy. 2023-11-09. RepQ: Generalizing Quantization-Aware Training for Re-Parametrized Architectures. https://arxiv.org/abs/2311.05317

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