A deterministic neuromorphic architecture with scalable time synchronization.

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.