English(EN) SPARC: Accurate Photonic Matrix Multiplication via Representative Vectors

SPARC方法提高了光子神经网络加速器的精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-14 05:00

研究人员开发了一种名为SPARC的新方法，以提高光子神经网络加速器的推理精度。该方法解决了这些系统中固有的低比特量化和模拟光子噪声造成的限制。SPARC通过识别相似激活向量的组，并以高精度数字计算代表性向量的点积，同时将剩余计算卸载到光子加速器来实现这一点。该技术保留了现有的光子计算管道，并在各种深度神经网络模型中展示了更高的精度。 AI

影响提高了AI硬件的效率和准确性，可能降低深度学习推理的功耗。

排序理由该集群包含一篇详细介绍提高硬件性能新方法的论文。[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) · Yasuhiro Fujiwara, Tatsuhiro Nakamori, Hideyuki Kawashima · 2026-11-14 05:00

SPARC：通过代表性向量实现精确的光子矩阵乘法

arXiv:2611.16561v1 Announce Type: new Abstract: Photonic neural network accelerators have emerged as a promising solution for reducing the power consumption of deep neural network (DNN) inference by executing matrix multiplication on analog photonic hardware. However, their infer…

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SPARC：通过代表性向量实现精确的光子矩阵乘法

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