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arXiv · 2404.11769

QGen: On the Ability to Generalize in Quantization Aware Training

MohammadHossein AskariHemmat·Ahmadreza Jeddi·Reyhane Askari Hemmat·Ivan Lazarevich·Alexander Hoffman·Sudhakar Sah·Ehsan Saboori·Yvon Savaria·Jean-Pierre David
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Abstract

Quantization lowers memory usage, computational requirements, and latency by utilizing fewer bits to represent model weights and activations. In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance. In particular, first, we develop a theoretical model for quantization in neural networks and demonstrate how quantization functions as a form of regularization. Second, motivated by recent work connecting the sharpness of the loss landscape and generalization, we derive an approximate bound for the generalization of quantized models conditioned on the amount of quantization noise. We then validate our hypothesis by experimenting with over 2000 models trained on CIFAR-10, CIFAR-100, and ImageNet datasets on convolutional and transformer-based models.

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MohammadHossein AskariHemmat, Ahmadreza Jeddi, Reyhane Askari Hemmat, Ivan Lazarevich, Alexander Hoffman, Sudhakar Sah, Ehsan Saboori, Yvon Savaria, Jean-Pierre David. 2024-04-19. QGen: On the Ability to Generalize in Quantization Aware Training. https://arxiv.org/abs/2404.11769

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