English(EN) ShatterQuant: Breaking Uniform Precision with Block-Wise Mixed-Precision on a Systolic Transformer Hardware Accelerator

ShatterQuant为Transformer硬件实现块状混合精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了ShatterQuant，一种用于神经网络混合精度量化的新颖框架，它允许在单个张量内的不同块分配独立的比特宽度。该方法与专为Transformer设计的定制硬件加速器集成，从而能够更精细地控制精度和计算效率。与现有方法相比，该系统在TOPS、面积效率和能效方面均有显著提升，同时在图像识别和生成任务上保持了可比的准确性。 AI

影响通过在块级别优化精度，实现更高效的Transformer模型硬件。

排序理由研究论文，详细介绍了混合精度量化的新颖软硬件协同设计。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Mikolaj Walczak, Edward Humes, Chao Fang, Marian Verhelst, Tinoosh Mohsenin · 2026-10-02 04:00

ShatterQuant：在脉动Transformer硬件加速器上通过块状混合精度打破均匀精度

arXiv:2610.00207v1 Announce Type: cross Abstract: Due to limited support for intra-tensor heterogeneous precision in conventional accelerators, neural network quantization remains largely restricted to per-tensor precision assignment. We present ShatterQuant, a hardware-software …

本文如何被排名

报道来源 [1]

ShatterQuant：在脉动Transformer硬件加速器上通过块状混合精度打破均匀精度

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