International Journal of Parallel Programming manuscript No. (will be inserted by the editor) Managing Burstiness and Scalability in Event-Driven Models on the SpiNNaker Neuromimetic System

Abstract Neural networks present a fundamentally different model of computation from the conventional sequential digital model, for which conventional hardware is typically poorly matched. However, a combination of model and scalability limi-tations has meant that neither dedicated neural chips nor FPGA’s have offered an entirely satisfactory solution. SpiNNaker introduces a different approach, the “neu-romimetic ” architecture, that maintains the neural optimisation of dedicated chips while offering FPGA-like universal configurability. This parallel multiprocessor em-ploys an asynchronous event-driven model that uses interrupt-generating dedicated hardware on the chip to support real-time neural simulation. Nonetheless, event han-dling, particularly packet servicing, requires careful and innovative design in order to avoid local processor congestion and possible deadlock. We explore the impact that spatial locality, temporal causality and burstiness of traffic have on network per-formance, using tunable, biologically similar synthetic traffic patterns. Having estab-lished the viability of the system for real-time operation, we use two exemplar neural models to illustrate how to implement efficient event-handling service routines tha