Advancements in spiking neural network communication and synchronization techniques for event-driven neuromorphic systems

Neuromorphic event-driven systems emulate the computational mechanisms of the brain through the utilization of spiking neural networks (SNN). Neuromorphic systems serve two primary application domains: simulating neural information processing in neuroscience and acting as accelerators for cognitive computing in engineering applications. A distinguishing characteristic of neuromorphic systems is their asynchronous or event-driven nature, but even event-driven systems require some synchronous time management of the neuron populations to guarantee sufficient time for the proper delivery of spiking messages. In this study, we assess three distinct algorithms proposed for adding a synchronization capability to asynchronous event-driven compute systems. We run these algorithms on POETS (Partially Ordered Event-Triggered Systems), a custom-built FPGA-based hardware platform, as a neuromorphic architecture. This study presents the simulation speed of SNNs of various sizes. We explore essential aspects of event-driven neuromorphic system design that contribute to efficient computation and communication. These aspects include varying degrees of connectivity, routing methods, mapping techniques onto hardware components, and firing rates. The hardware mapping and simulation of up to eight million neurons, where each neuron is connected to up to one thousand other neurons, are presented in this work using 3072 reconfigurable processing cores, each of which has 16 hardware threads. Using the best synchronization and communication methods, our architecture design demonstrates 20-fold and 16-fold speedups over the Brian simulator and one 48-chip SpiNNaker node, respectively. We conclude with a brief comparison between our platform and existing large-scale neuromorphic systems in terms of synchronization, routing, and communication methods, to guide the development of future event-driven neuromorphic systems.