动态性能和功率优化与异质处理内存对边缘设备上AI应用程序的边缘设备
Sangmin Jeon1, Kangju Lee1, Kyeongwon Lee1
1Department of Intelligent Semiconductor Engineering, Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea.
Micromachines
|October 26, 2024
概括
本研究引入了一种具有异质模块的新型内存处理 (PIM) 技术,以动态优化人工智能 (AI) 性能和边缘设备的能源效率,实现显著的能源节约.
科学领域:
- 计算机工程 计算机工程
- 人工智能的人工智能
- 能源效率 能源效率 能源效率
背景情况:
- 人工智能和物联网 (IoT) 的进步推动人工智能应用到边缘设备.
- 在边缘设备上的动态AI工作负载中平衡性能和能源效率是具有挑战性的.
- 现有的解决方案难以适应实时计算负载变化.
研究的目的:
- 提出一种新的内存处理 (PIM) 技术,用于边缘设备上AI应用程序的动态优化.
- 在不同的计算负载下解决性能-能源效率权衡问题.
- 通过智能资源管理,增强边缘的AI能力.
主要方法:
- 开发了一种异质的PIM架构,包括高性能PIM (HP-PIM) 和低功耗PIM (LP-PIM) 模块.
- 设计了一个数据配置优化算法,以预测工作负载变化和分配数据.
- 在嵌入式处理器中实现了PIM架构,并执行了FPGA原型设计.
主要成果:
- 不同质的PIM架构根据工作负载需求动态调整数据处理.
- 数据配置算法有效地分配数据以优化能源效率.
- 实验结果显示,在不同工作负载的AI应用中,节能高达29.54%.
结论:
- 提出的异质PIM技术有效地优化了边缘设备上的AI的性能和能源消耗.
- 动态的工作负载适应对于高效的边缘AI部署至关重要.
- 这种PIM解决方案为可持续的边缘AI提供了一个有希望的方法.
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