驼:适应性代码优化用于加速深度神经网络编译
Byung Hoon Ahn1, Prannoy Pilligundla1, Amir Yazdanbakhsh2
1University of California, San Diego.
概括
驼是一种新的强化学习方法,通过学习自适应抽样策略来加速神经网络代码的优化. 这大大减少了编译时间,并提高了深度网络推理性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 当前的神经网络执行方法依赖于非最佳的,耗时的技术,如手动优化的库或遗传算法.
- 这些方法通常涉及频繁,昂贵的硬件测量,阻碍了效率和创新.
研究的目的:
- 开发一种适应性解决方案,用于神经网络中的快速代码优化.
- 为了加快寻找最佳代码的速度,并提高未见的设计空间的输出性能.
主要方法:
- 利用强化学习 (RL) 来实现更快的优化趋同.
- 开发一个适应性采样算法,优先考虑代表性的硬件测量.
- 纳入领域知识启发逻辑来提高样本质量.
主要成果:
- 与AutoTVM相比,Chameleon在优化时间中实现了4.45倍的加快速度.
- 对于现代深度网络来说,推断时间有5.6%的改善.
- 成功地适应了以前看不见的设计空间,以实现高效的代码优化.
结论:
- 驼为神经网络代码优化提供了一种更高效,更有效的方法.
- 适应性采样和RL集成显著减少了优化时间,提高了性能.
- 这种方法通过能够更快地代和部署各种神经网络架构来促进创新.
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