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Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

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Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems.

Thomas Dalgaty¹, Filippo Moro¹, Yiğit Demirağ²

¹CEA, LETI, Université Grenoble Alpes, Grenoble, France.

Nature Communications

|January 3, 2024

Summary

We developed Mosaic, a novel neuromorphic architecture using memristors for efficient small-world Spiking Neural Networks (SNNs). This design significantly boosts routing efficiency for edge AI applications.

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Area of Science:

Neuromorphic Engineering
Artificial Intelligence
Computer Architecture

Background:

The brain exhibits a small-world network topology, optimizing information processing.
Current artificial neural networks often do not fully leverage small-world principles.
Efficient hardware for Spiking Neural Networks (SNNs) is crucial for advanced AI.

Purpose of the Study:

To introduce the neuromorphic Mosaic architecture for efficient implementation of small-world SNNs.
To demonstrate a non-von Neumann, systolic architecture with in-memory computing and routing.
To validate the performance and scalability of the Mosaic architecture for edge computing.

Main Methods:

Designed and fabricated neuromorphic Mosaic building blocks using 130 nm CMOS technology with integrated memristors.
Implemented distributed memristors for in-memory computing and routing.
Utilized Spiking Neural Network (SNN) models with small-world graph topologies.

Main Results:

Experimental demonstration of Mosaic's building blocks.
Achieved at least one order of magnitude higher routing efficiency compared to other SNN hardware platforms.
Demonstrated competitive accuracy on various edge computing benchmarks.

Conclusions:

Mosaic effectively implements small-world graph topologies for SNNs using in-memory computing and routing.
The architecture offers significant improvements in routing efficiency for SNN hardware.
Mosaic presents a scalable solution for distributed, spike-based edge computing systems.