C2-LSM:一个基于Storm-NoC的神经形态处理器,用于使用立方体集群拓学的高精度液态机器
IEEE transactions on biomedical circuits and systems
|November 24, 2025
概括
我们介绍了C2-LSM,这是一款新型的神经形态处理器,用于液态机器 (LSM) 的立方集群拓. 这种设计在时空任务上实现了高精度和高效率,优于现有的LSM处理器.
科学领域:
- 神经形态工程的神经形态工程
- 尖端神经网络的神经网络.
- 储水库计算 储水库计算
背景情况:
- 液态机器 (LSMs) 是尖端神经网络 (SNNs) 的一种变体,以其低训练复杂性而闻名.
- 生物大脑展现出生物大脑的表现.
- 小世界 - - 小世界.
- 网络结构激发了高效的神经处理.
研究的目的:
- 提出C2-LSM,一种通过算法硬件共同设计开发的神经形态处理器.
- 通过使用LSM来提高各种时空任务的准确性和效率.
主要方法:
- 算法级设计:引入了一种新的储存层,采用由生物神经网络启发的立方集群拓.
- 硬件实现:在AMD Virtex UltraScale+ VCU129 FPGA上开发了一个定制的C2-LSM处理器,具有运行时配置.
- 芯片上的网络 (NoC):集成了一个Storm路由算法来优化尖端事件传输.
主要成果:
- 实现了高分类准确度:98.02%在MNIST上,94.26%在N-MNIST上,93.00%在FSDD上.
- 与最近进行基准测试的LSM神经形态处理器相比,表现出卓越的性能.
- 在MNIST上实现了1155 FPS推断和1154 FPS学习速度,功率效率为103 GSOPS/W.
结论:
- C2-LSM处理器实现了对时空任务的最先进的准确性和效率.
- 算法-硬件联合设计对于开发高性能神经形态系统是有效的.
- 立方集群拓和优化的NoC有助于提高C2-LSM的性能.
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