能源意识的生物启发的增强学习系统架构,用于实时自主边缘应用程序
Joshua Ifeanyi Okonkwo1, Mohamed S Abdelfattah2, Peyman Mirtaheri3,4
1Biomedical Engineering MS Program, Oslo Metropolitan University, Oslo, Norway.
Frontiers in neuroscience
|October 8, 2024
概括
这项研究介绍了一种新的生物启发强化学习 (RL) 系统,用于尖端神经网络 (SNN),该系统可显著减少边缘AI的能源使用. 新架构为自主系统实现了相当大的功率和能源节约.
科学领域:
- 神经形态工程的神经形态工程
- 人工智能 (AI) 是一种人工智能.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 节能的人工智能对智能自动化,智能电路和自主机器人至关重要.
- 尖端神经网络 (SNN) 为低功耗人工智能提供生物灵感的解决方案.
- 现有的SNN强化学习 (RL) 模型正在为边缘应用进行扩展.
研究的目的:
- 为SNN提供一种新的生物灵感RL系统架构.
- 在边缘人工智能应用中实现显著的能源节约.
- 为了保持实时的自主处理和准确性.
主要方法:
- 开发了一种生物启发的RL系统架构,模仿了像突触标记和局部激活这样的脑功能.
- 模拟的特征,如探索方案和突触和.
- 在英特尔MAX10现场可编程网关阵列 (FPGA) 上合成,模拟和测试设计.
主要成果:
- 与实时上下文学习的最先进技术相比,平均功率减少了25倍.
- 由于执行时间的改善,实现了940倍更低的能耗.
- 在硬件中成功建模了类似于大脑的特征.
结论:
- 生物启发的SNN边缘架构提供了可观的能源效率优势.
- 该系统满足了对上下文依赖任务的实时处理和准确性要求.
- 这种方法使得具有增强边缘智能的更智能微系统成为可能.
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