马赛克:用于基于小世界尖峰的神经形态系统的内存计算和路由.
Thomas Dalgaty1, Filippo Moro1, Yiğit Demirağ2
1CEA, LETI, Université Grenoble Alpes, Grenoble, France.
Nature communications
|January 3, 2024
概括
我们开发了Mosaic,这是一个新的神经形态架构,使用memristors进行高效的小世界尖端神经网络 (SNN). 这种设计显著提高了边缘AI应用程序的路由效率.
科学领域:
- 神经形态工程的神经形态工程
- 人工智能的人工智能
- 计算机架构 计算机架构
背景情况:
- 大脑表现出一个小世界网络拓,优化信息处理.
- 当前的人工神经网络往往不能充分利用小世界原则.
- 对尖端神经网络 (SNN) 的高效硬件对于先进的人工智能至关重要.
研究的目的:
- 引入神经形态的马赛克架构,以有效地实现小世界SNN.
- 为了展示一个非·诺伊曼的,系统式架构与内存计算和路由.
- 为了验证Mosaic架构的性能和可扩展性,用于边缘计算.
主要方法:
- 设计和制造的神经形态马赛克构建块使用130纳米CMOS技术与集成的memristors.
- 实现了用于内存计算和路由的分布式memristor.
- 利用了带有小世界图形拓的尖端神经网络 (SNN) 模型.
主要成果:
- 莫赛克的构建块的实验演示.
- 与其他SNN硬件平台相比,至少达到一个数量级的更高路由效率.
- 在各种边缘计算基准上表现出具有竞争力的准确性.
结论:
- 马赛克有效地实现了使用内存计算和路由的SNN的小世界图形拓.
- 该架构为SNN硬件的路由效率提供了显著的改进.
- 马赛克为分布式,基于尖峰的边缘计算系统提供了一个可扩展的解决方案.
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