SNN4Agents:一个框架,用于为自主代理开发高能效的体内尖端神经网络
Rachmad Vidya Wicaksana Putra1, Alberto Marchisio1, Muhammad Shafique1
1eBrain Lab, Division of Engineering, New York University (NYU) Abu Dhabi, Abu Dhabi, United Arab Emirates.
Frontiers in robotics and AI
|August 12, 2024
概括
我们介绍SNN4Agents,这是一个优化尖端神经网络 (SNN) 的框架,用于节能自主代理. 这种方法显著减少了内存,加快了处理速度,提高了能源效率,同时保持了嵌入式AI应用程序的高精度.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 神经形态工程的神经形态工程
- 计算机视觉 计算机视觉
背景情况:
- 像车辆和机器人这样的自主代理需要较低的功率才能长时间运行.
- 尖端神经网络 (SNN) 为这些代理提供了高效,生物启发的计算.
- 在自主系统中对嵌入式SNNs的系统优化尚未得到充分发展.
研究的目的:
- 提出SNN4Agents,这是能源效率高的嵌入式SNN的新框架.
- 在自主代理中部署SNNs的优化阶段.
- 通过优化SNN来提高自主代理的性能.
主要方法:
- 开发了SNN4Agents框架,包括重量量化,时间步骤减少和注意力窗口减少.
- 应用优化技术来设计高能效的嵌入式SNNs.
- 通过基于事件的汽车识别使用案例来评估框架.
主要成果:
- 在NCARS数据集上,SNN4Agents实现了84.12%的准确性.
- 已证明可以节省68.75%的内存,加快3.58倍的速度和提高4.03倍的能效.
- 展示了精度,延迟,内存和能源消耗之间的有效权衡.
结论:
- SNN4Agents提供了一种系统的方法来优化体现SNN的自主代理.
- 该框架能够显著提高能源效率和性能.
- 为在自主系统中广泛采用节能SNNs铺平了道路.
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