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比特缩放:通过预测混合精度网络的多尺度增长来简化神经网络压缩
Yuehao Li1, Haifang Jian2, Hongchang Wang2
1College of Materials Science and Opto-Electronic Technology, University of the Chinese Academy of Sciences, Beijing, 101408, China; Laboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.
概括
通过联合优化模型规模和混合精度量化,BitScaling有效地压缩神经网络. 这种新的框架显著加快了搜索速度,降低了内存使用量,同时保持了高精度.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 联合优化神经网络规模和量子化提供了优越的压缩,但面临着巨大的搜索空间.
- 由于联合压缩的组合复杂性,现有的方法难以实用.
研究的目的:
- 开发一个统一的框架,BitScaling,用于神经网络的高效共压缩.
- 将计算缩放规律扩展到混合精度量子化网络,以更好地预测最佳配置.
主要方法:
- 扩展计算缩放规律,使测试损失,模型规模和比特宽度在固定预算 (每秒数十亿次操作 - BOPs) 下成为统一的连续函数.
- 介绍了BitScaling,一个共压缩框架,利用缩小尺寸的超级网络代理来实现高效的混合精度量子化搜索.
- 嵌入量子化意识模型增长用于额外的数据拟合和预测最佳缩放比率和比特分配.
主要成果:
- 比特缩放实现了比现有的混合精度定量化 (MPQ) 方法快6.82倍的搜索速度.
- 与传统的MPQ方法相比,显示的内存使用率降低了85.77%.
- 在ImageNet的极低预算限制下,与最先进的联合压缩方法相匹配或超越,在top-1精度下.
结论:
- 比特缩放为神经网络共压缩提供了一种高效和有效的解决方案.
- 该框架允许在压缩效率和模型性能之间进行优异的权衡,特别是在资源有限的环境中.
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