English(EN) Realizing Low-Precision Storage and Sparse Execution in a Folded FPGA Speech Enhancer

折叠式FPGA语音增强器通过低精度存储和稀疏执行实现效率提升

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一种新颖的方法来优化现场可编程门阵列（FPGA）上的语音增强加速器，重点关注低精度存储和稀疏执行。他们的方法在Kria KV260上实现，展示了计算和能耗的显著降低。通过解耦精度、权重存储和调度工作，他们实现了卷积MACs减少47.3%，帧时间减少45%，从而提高了语音质量。 AI

影响这项研究可能带来更高效的音频应用的设备端AI处理。

排序理由该集群包含一篇详细介绍新研究方法和实现的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Cl\'ement Laroche · 2026-10-28 04:00

一种折叠式FPGA语音增强器中的低精度存储和稀疏执行实现

arXiv:2610.35144v1 Announce Type: cross Abstract: Hardware-aware training often optimizes FPGA resources, latency, or bit operations, but folding changes what compression saves: shared arithmetic decouples precision, weight storage, and scheduled work. We derive training objectiv…