Novel Neuromorphic Artificial Neural Network Circuit Architecture


A technical paper titled “Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems” was published by researchers at CEA-LETI Université Grenoble Alpes, University of Zurich and ETH Zurich.


“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.”

Find the technical paper here. Published January 2024.

Dalgaty, T., Moro, F., Demirağ, Y. et al. Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems. Nat Commun 15, 142 (2024). https://doi.org/10.1038/s41467-023-44365-x

Further Reading
Increasing AI Energy Efficiency With Compute In Memory
How to process zettascale workloads and stay within a fixed power budget.
Partitioning Processors For AI Workloads
General-purpose processing, and lack of flexibility, are far from ideal for AI/ML workloads.

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