# Block-Wise Dynamic-Precision Neural Network Training Acceleration via Online Quantization Sensitivity Analytics

Source: [Block-Wise Dynamic-Precision Neural Network Training Acceleration via Online Quantization Sensitivity Analytics](<https://www.sota2.com/research/paper/68fa6dc31e209131cea3bef9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:43:12.182Z

Authors: Ruoyang Liu, Chenhan Wei, Yixiong Yang, Wenxun Wang, Huazhong Yang, Yongpan Liu

Published: 2022-10-31T03:54:16.000Z

## Abstract

Data quantization is an effective method to accelerate neural network training and reduce power consumption. However, it is challenging to perform low-bit quantized training: the conventional equal-precision quantization will lead to either high accuracy loss or limited bit-width reduction, while existing mixed-precision methods offer high compression potential but failed to perform accurate and efficient bit-width assignment. In this work, we propose DYNASTY, a block-wise dynamic-precision neural network training framework. DYNASTY provides accurate data sensitivity information through fast online analytics, and maintains stable training convergence with an adaptive bit-width map generator. Network training experiments on CIFAR-100 and ImageNet dataset are carried out, and compared to 8-bit quantization baseline, DYNASTY brings up to $5.1\\times$ speedup and $4.7\\times$ energy consumption reduction with no accuracy drop and negligible hardware overhead.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.17047>)

[PDF](<https://arxiv.org/pdf/2210.17047.pdf>)