结合神经架构搜索和重量重塑,在多感应手套中优化嵌入式分类器
Hiba Al Youssef1,2, Sara Awada1, Mohamad Raad1
1Department of Computer and Communication Engineering, Lebanese International University, Beirut 1105, Lebanon.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究使用硬件意识的神经架构搜索 (HW-NAS) 和模型优化优化了可穿戴设备的嵌入式神经网络. 综合方法显著提高了能源自主系统的效率.
科学领域:
- 人工智能的人工智能
- 嵌入式系统 嵌入式系统
- 可穿戴技术可穿戴技术
背景情况:
- 智能传感系统对于机器人和人机交互中的可穿戴设备至关重要.
- 由于资源限制,能源自主性是这些系统面临的关键挑战.
- 优化嵌入式神经网络以提高准确性和效率至关重要.
研究的目的:
- 通过将硬件意识神经架构搜索 (HW-NAS) 与优化技术相结合,开发用于多感应手套的节能分类器.
- 为资源有限的可穿戴设备在嵌入式神经网络中平衡精度和效率.
- 为了证明组合HW-NAS和优化能源自主系统的有效性.
主要方法:
- 硬件意识的神经架构搜索 (HW-NAS) 用于自动导出适合NUCLEO-F401RE板的1D-CNN模型.
- 应用了模型优化技术,包括重量重塑和量化,以进一步减少资源使用.
- 综合方法在三个数据集上使用多传感手套进行了评估.
主要成果:
- 与仅NAS的基线相比,优化的模型实现了更好的分类准确性.
- 在推断时间 (平均75%),闪存 (平均69%) 和RAM (平均45%) 中观察到显著的减少.
- 综合方法有效地减少了模型大小,内存足迹和延迟.
结论:
- 将HW-NAS与优化技术集成在开发可穿戴设备高效分类器方面非常有效.
- 这种方法为创造实用,能源自主可穿戴传感系统铺平了道路.
- 该研究表明,在严格的资源限制下,优化嵌入式神经网络的可行策略.
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