BriefGPT.xyz
Dec, 2017
移动设备上高性能超低精度卷积
High performance ultra-low-precision convolutions on mobile devices
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Andrew Tulloch, Yangqing Jia
TL;DR
通过对ARMv7设备上现代深度学习工作负载所需的核心基本操作进行开源实现和全面分析,我们展示了与现有市场上的float32和int8基准相比,使用大于4位精度的最先进的超低精度技术可获得4倍至20倍的加速度。
Abstract
Many applications of
mobile deep learning
, especially
real-time computer vision
workloads, are constrained by computation power. This is particularly true for workloads running on older consumer phones, where a t
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