English(EN) SparsePixels: Efficient Convolution for Sparse Data on FPGAs

SparsePixels框架提升稀疏数据在FPGA上的CNN推理性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-30 04:00

研究人员开发了SparsePixels框架，旨在优化稀疏数据在FPGA上的卷积神经网络（CNN）推理。该方法选择性地处理活动像素，与标准的密集CNN相比，显著减少了计算量和延迟。对于LArTPC图像，SparsePixels实现了73倍的加速，将推理时间缩短至一微秒以内，同时保持了高性能并符合FPGA资源限制。 AI

影响该框架有望在具有稀疏数据的专业硬件中实现更快、更高效的AI推理。

排序理由该集群包含一篇学术论文，详细介绍了一种新的高效计算技术框架。[lever_c_research降级：ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Ho Fung Tsoi, Dylan Rankin, Vladimir Loncar, Philip Harris · 2026-06-30 04:00

SparsePixels: 稀疏数据在FPGA上的高效卷积

arXiv:2512.06208v3 Announce Type: replace-cross Abstract: Inference of standard convolutional neural networks (CNNs) on FPGAs often incurs high latency and a long initiation interval due to the deep nested loops required to densely convolve every input pixel regardless of its fea…

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SparsePixels: 稀疏数据在FPGA上的高效卷积

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