ArticleNature communications2024
Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed.
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Voltage-Programmable Switching in Halide Perovskite Memristors by Asymmetric Contact Engineering.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- TEA-LWNet: a lightweight multispectral feature enhancement network based on RGB-NIR fusion for detection of tea leaf diseases and pests.Frontiers in plant science · 2026Article
- Neuromorphic Visual Receptive Field Hardware with Vertically Integrated Indium-Gallium-Zinc-Oxide Optoelectronic Memristors over Silicon Neuron Transistors.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Neuromorphic computing for robotic vision: algorithms to hardware advances.Communications engineering · 2025Review
- A neuromorphic processor with on-chip learning for beyond-CMOS device integration.Nature communications · 2025Article
- Topology optimization of random memristors for input-aware dynamic SNN.Science advances · 2025Article
- The road to commercial success for neuromorphic technologies.Nature communications · 2025Review
- Dynamics of specialization in neural modules under resource constraints.Nature communications · 2025Article
- DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays.Nature communications · 2024Article
- Memristor-based model of neuronal excitability and synaptic potentiation.Frontiers in neuroscience · 2024Article
- A CMOS-memristor hybrid system for implementing stochastic binary spike timing-dependent plasticity.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.