The neuromorphic Mosaic: in-memory computing and routing for small-world graphical networks

Abstract

The connectivity in the brain is locally dense and globally sparse - giving rise to a small-world graph. This is a principle that has persisted during the evolution of many species - indicating a universal solution to the efficient routing of information. However, existing circuit architectures for artificial neural networks neither leverage this organization nor do they efficiently support small-world neural network models. Here, we propose the neuromorphic Mosaic: a non-von Neumann systolic architecture that uses distributed memristors, not only for in-memory computing, but also for in-memory routing, to efficiently implement small-world graph topologies. We design, fabricate, and experimentally demonstrate the building blocks of this architecture, using integrated memristors with 130 nm CMOS technology. We demonstrate that neural networks implemented following this approach can achieve competitive accuracy figures compared to equivalent unconstrained and full-precision networks, for three real-time benchmarks: classification of electrocardiography signals, keyword spotting and motor control via reinforcement learning. The Mosaic shows improvements between one and four orders of magnitude, compared to other event-based neuromorphic architectures for routing events across the network. The Mosaic opens up a new scalable approach for designing edge AI systems based on distributed computing and in-memory routing, offering a natural platform onto which architectures inspired by biological nervous systems can be readily mapped.