tinyHLS:一个新的开源高层合成工具,针对人工神经网络推断的硬件加速器.
Ingo Hoyer1, Alexander Utz1, Christoph Hoog Antink2
1Fraunhofer Institute for Microelectronic Circuits and Systems IMS, Duisburg, Germany.
Physiological measurement
|January 10, 2025
概括
一个新的Python包,tinyHLS,通过将AI模型转换为硬件加速器,使可穿戴设备上的实时AI分析成为可能. 这大大提高了处理速度和能源效率,用于像ECG监控这样的应用.
科学领域:
- 人工智能的人工智能
- 硬件加速器 硬件加速器
- 可穿戴技术可穿戴技术
背景情况:
- 像智能手表这样的可穿戴设备在生物信号监测 (例如,心电图) 中表现出色,但缺乏实时内置分析的能力.
- 有限的电源限制了边缘设备对复杂AI任务的计算能力.
研究的目的:
- 介绍tinyHLS,一个新的Python包,用于将AI模型转换为硬件加速器.
- 直接在资源有限的可穿戴设备上实现高效的实时数据分析.
- 促进人工智能融入硬件加速器的开发工作流程.
主要方法:
- 开发了 tinyHLS,这是一个Python包,可以将AI模型 (TensorFlow Keras) 翻译成硬件描述语言代码.
- 使用基于模板的硬件编译器,支持各种神经网络层和激活功能.
- 专注于1D数据,特别是与生物信号处理相关的时间序列数据.
主要成果:
- 生成的硬件加速器显示处理速度增加了62倍,能源效率提高了4.5倍.
- 通过ECG数据检测心房动的验证加速器.
- 使用商业ASIC设计工具确保代码质量和可合成性.
结论:
- tinyHLS提供了一个开源的多功能解决方案,用于开发边缘设备上AI的硬件加速器.
- 该包支持无集成到现有的AI开发工作流程中.
- 潜在的应用包括边缘计算,云服务和增强可穿戴生物信号分析.
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