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一个低功率的通用矩阵乘法加速器,具有稀疏的重量和输出静止数据流
1Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou 311100, China.
Micromachines
|January 25, 2025
概括
本研究介绍了一种新的稀疏通用矩阵乘法 (GEMM) 加速器,用于在资源有限的设备上高效的机器学习. 该方法通过优化数据移动和缓冲器利用来提高计算和能源效率.
科学领域:
- 计算机科学 计算机科学
- 机器学习硬件 机器学习硬件
背景情况:
- 一般矩阵乘法 (GEMM) 是计算密集型的,限制其在资源有限的环境中使用.
- 现有的GEMM数据再利用方法无法充分利用数据流动的潜在减少.
研究的目的:
- 开发一个稀疏的GEMM加速器,提高边缘设备上机器学习的效率.
- 为了应对硬件中存储和处理稀疏矩阵的挑战.
主要方法:
- 引入了一个重量和输出静止 (WOS) 数据流和分布式缓冲区架构,用于稀疏的GEMM.
- 为压缩的GEMM开发了一个可适应的映射方案,并为权重开发了一个离线的稀疏度意识的混合策略.
- 实施了一种低成本的稀疏计算方法,使用全球共享的输入来实现高吞吐量.
主要成果:
- 稀疏的GEMM加速器以压缩格式处理矩阵,消除了芯片上的重量和部分总和转移.
- 精益求精的混合策略平衡了缓冲器利用率,并最大限度地减少了不规则重量矩阵的浪费.
- 与现有方法相比,FPGA实验显示了1.73倍更好的计算效率和1.36倍更高的能源效率.
结论:
- 建议的稀疏GEMM加速器有效地减少了数据的移动,并提高了机器学习的硬件效率.
- 新的数据流,缓冲架构和重量管理策略使稀疏矩阵的有效处理成为可能.
- 这项工作为在资源有限的平台上部署计算要求高的机器学习模型提供了可行的解决方案.
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