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arXiv · 2501.04517

Histogram-Equalized Quantization for logic-gated Residual Neural Networks

Van Thien Nguyen·William Guicquero·Gilles Sicard
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Abstract

Adjusting the quantization according to the data or to the model loss seems mandatory to enable a high accuracy in the context of quantized neural networks. This work presents Histogram-Equalized Quantization (HEQ), an adaptive framework for linear symmetric quantization. HEQ automatically adapts the quantization thresholds using a unique step size optimization. We empirically show that HEQ achieves state-of-the-art performances on CIFAR-10. Experiments on the STL-10 dataset even show that HEQ enables a proper training of our proposed logic-gated (OR, MUX) residual networks with a higher accuracy at a lower hardware complexity than previous work.

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Van Thien Nguyen, William Guicquero, Gilles Sicard. 2025-01-08. Histogram-Equalized Quantization for logic-gated Residual Neural Networks. https://doi.org/10.1109/iscas48785.2022.9937290

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