English(EN) Decode Late, Multiply Once: Rethinking the Dot Product for Quantized Weights

新方法减少量化神经网络的点积计算

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员提出了一种计算量化神经网络中点积的新颖方法，旨在减少计算开销。该技术重构了点积，使其使用加法以及有限次数的解码和乘法，显著降低了乘法器的数量。这种方法对于整数运算是精确的，并且与各种解码器类型兼容，为本地权重提供了潜在的节能和内存需求降低。 AI

影响有望提高量化神经网络部署的效率并降低计算成本。

排序理由详细介绍神经网络新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Jonathan Tapson · 2026-10-27 04:00

延迟解码，一次乘法：重新思考量化权重的点积

arXiv:2610.31913v1 Announce Type: new Abstract: Large neural networks are increasingly deployed with 1-4 bit weights and 16- or 32-bit activations, yet their dot products still use one multiplier per element. We show that a length-n dot product with k-bit weight codes can be comp…