基于超导环的流量突触装置的评估,用于节能的神经形态计算
Ashwani Kumar1, Uday S Goteti2, Ertugrul Cubukcu3
1Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA, United States.
Frontiers in neuroscience
|March 3, 2025
概括
具有约瑟夫森结点的超导循环为节能神经形态计算提供了一种新的方法. 这种流量突触阵列在计算任务中显示了大约100倍能耗降低的潜力.
科学领域:
- 物理 物理学 物理
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 摩尔定律的局限性需要新的计算范式.
- 神经形态计算旨在模仿大脑的效率.
- 超导电路为低功耗计算提供了潜力.
研究的目的:
- 为了评估使用约瑟夫森连接器进行节能神经形态计算的无序超导循环的性能.
- 为了证明使用流量状态用于突触重量存储和调制的可行性.
- 探索这些设备在矩阵向量乘法和学习任务中的应用.
主要方法:
- 利用三条相互连接的超导环与约瑟夫森结点来存储突触重量作为被困的流量状态.
- 通过控制输入的流量信号来调节突触重量.
- 实现矩阵-向量乘法运算,使用数组的流量突触装置.
- 在MNIST数据集上调查在线学习的能源效率.
主要成果:
- 超导环中的流状态有效地代表和调节突触重量.
- 流量突触装置的数组可以执行矩阵向量乘法.
- 与MNIST数据集学习的最先进的突触设备相比,拟议的流体突触阵列可实现大约100倍的能源消耗降低.
结论:
- 带有约瑟夫森结的无序超导循环为神经形态计算提供了一种可行且高能效的方法.
- 这种概念验证为开发使用超导材料的高速,节能的神经形态系统铺平了道路.
- 流量突触技术在克服当前计算架构的局限性方面具有重大前景.
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