AoCStream:全芯片CNN加速器,具有基于流的线路缓冲架构和加速器意识的修剪
Hyeong-Ju Kang1, Byung-Do Yang2
1School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeol-ro, Dongnam-gu, Cheonan 31253, Republic of Korea.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了EdgeAI和TinyML中的卷积神经网络 (CNN) 的记忆减少技术. 这些方法使得在资源有限的设备上只使用芯片内存实现高效的CNN实现.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 嵌入式系统 嵌入式系统
背景情况:
- 卷积神经网络 (CNN) 对EdgeAI和TinyML至关重要,但需要大量的内存.
- 外部内存需求阻碍了在资源有限的EdgeAI和TinyML应用中使用CNN的可行性.
研究的目的:
- 在算法和架构层面为CNN提出新的记忆减少方法.
- 为了实现高性能CNN只使用实际设备的芯片内存.
主要方法:
- 实现了加速器意识的修剪,以减少重量记忆.
- 提出了一种基于流的线路缓冲架构,用于激活内存的减少.
- 采用管道式方法,使用专用层块和线路缓冲器代替框架缓冲器.
主要成果:
- 成功减少中间数据存储大小.
- 在低端FPGA上展示了对象检测CNN (MobileNetV1/V2,SSDLite变体) 的实现.
- 在不需要外部存储器的情况下实现了实现.
结论:
- 拟议的减少内存技术对于在资源有限的EdgeAI和TinyML环境中部署CNN是有效的.
- 在芯片内存实现CNN是可行的实际应用程序,如对象检测在低端FPGA.
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