ArticleNature communications2025
A deterministic neuromorphic architecture with scalable time synchronization.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- A deterministic neuromorphic architecture with scalable time synchronization.Nature communications · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
Custom integrated circuits modeling biological neural networks serve as tools for studying brain computation and platforms for exploring new architectures and learning rules of artificial neural networks. Time synchronization across network units is an important aspect of these designs to ensure reproducible results and maintain hardware-software equivalence. Current approaches rely on global synchronization protocols, which fundamentally limit system scalability. To overcome this, we develop NeuroScale, a decentralized and scalable neuromorphic architecture that uses local, aperiodic synchronization to preserve determinism without global coordination. Cores of co-localized compute and memory elements model neural and synaptic processes, including spike filtering operations, subthreshold neural dynamics, and online Hebbian learning rules. Multiple cores communicate via spikes across a routing mesh, using distributed event-driven synchronization to efficiently scale to large networks. We compare this synchronization protocol to the global barrier synchronization approaches of IBM TrueNorth and Intel Loihi, demonstrating NeuroScale's advantages for large system sizes.
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Registered trials
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