Dalgaty Thomas, Moro Filippo, Demirağ Yiğit, De Pra Alessio, Indiveri Giacomo, Vianello Elisa, Payvand Melika
CEA, LETI, Université Grenoble Alpes, Grenoble, France.
Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
Nat Commun. 2024 Jan 2;15(1):142. doi: 10.1038/s41467-023-44365-x.
The brain's connectivity is locally dense and globally sparse, forming a small-world graph-a principle prevalent in the evolution of various species, suggesting a universal solution for efficient information routing. However, current artificial neural network circuit architectures do not fully embrace small-world neural network models. Here, we present the neuromorphic Mosaic: a non-von Neumann systolic architecture employing distributed memristors for in-memory computing and in-memory routing, efficiently implementing small-world graph topologies for Spiking Neural Networks (SNNs). We've designed, fabricated, and experimentally demonstrated the Mosaic's building blocks, using integrated memristors with 130 nm CMOS technology. We show that thanks to enforcing locality in the connectivity, routing efficiency of Mosaic is at least one order of magnitude higher than other SNN hardware platforms. This is while Mosaic achieves a competitive accuracy in a variety of edge benchmarks. Mosaic offers a scalable approach for edge systems based on distributed spike-based computing and in-memory routing.
大脑的连接性在局部是密集的，在全局是稀疏的，形成了一个小世界图——这是一个在各种物种进化中普遍存在的原则，暗示了一种高效信息路由的通用解决方案。然而，当前的人工神经网络电路架构并未完全采用小世界神经网络模型。在此，我们展示了神经形态的Mosaic：一种非冯·诺依曼脉动架构，采用分布式忆阻器进行内存计算和内存路由，能有效地为脉冲神经网络（SNN）实现小世界图拓扑结构。我们使用130纳米CMOS技术的集成忆阻器设计、制造并通过实验展示了Mosaic的构建模块。我们表明，由于在连接性方面强化了局部性，Mosaic的路由效率比其他SNN硬件平台至少高一个数量级。与此同时，Mosaic在各种边缘基准测试中实现了具有竞争力的准确率。Mosaic为基于分布式脉冲计算和内存路由的边缘系统提供了一种可扩展的方法。
