对边缘AI神经形态电路的深度和尖端神经网络进行比较的审查
Pietro M Ferreira1, Siqi Wang2,3, Yueyuan Gao4
1University Savoie Mont Blanc, University Grenoble Alpes, Grenoble INP, CNRS, CROMA, Chambéry, France.
Frontiers in neuroscience
|October 20, 2025
概括
边缘人工智能通过使用深度神经网络 (DNN) 或尖端神经网络 (SNN) 将人工智能带到电子设备中. 本综述比较了数字和模拟实现,重点关注先进的人工智能硬件的能源效率和性能权衡.
科学领域:
- 电气工程和计算机科学
- 人工智能和机器学习
背景情况:
- 边缘人工智能将神经网络集成到电子电路中,利用深度神经网络 (DNN) 或尖端神经网络 (SNN).
- DNN提供了高精度,但需要大量的计算资源和功率.
- 通过生物灵感,事件驱动的设计,SNN提供了卓越的能源效率,但面临着不那么成熟的培训工具的挑战.
研究的目的:
- 审查数字和模拟边缘AI实施方案.
- 为Edge AI概述设备架构和神经元模型.
- 分析对边缘人工智能硬件的能源,区域和集成技术的权衡.
主要方法:
- 对现有的数字和模拟边缘人工智能硬件的调查.
- 对与边缘AI相关的各种神经元模型的分析.
- 评估性能指标,包括能源 (J/OP) 和面积 (μm2/OP).
主要成果:
- 在DNN和SNN之间比较计算强度和功耗.
- 用于边缘人工智能应用程序的设备架构的表征.
- 确定影响边缘人工智能技术选择的关键权衡.
结论:
- 尽管目前培训工具的局限性,但SNN是朝着节能边缘人工智能发展的有希望的途径.
- 数字和模拟实现之间的选择取决于特定的应用程序对精度,功率和面积的要求.
- 培训方法和整合技术的进一步进步对于实现Edge AI的全部潜力至关重要.
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