Scalable event-driven native parallel processing: the SpiNNaker neuromimetic system

Abstract

Neural networks present a fundamentally different model of computation from the conventional sequential digital model. Modelling large networks on conventional hardware thus tends to be inefficient if not impossible. Neither dedicated neural chips, with model limitations, nor FPGA implementations, with scalability limitations, offer a satisfactory solution even though they have improved simulation performance dramatically. SpiNNaker introduces a different approach, the "neuromimetic" architecture, that maintains the neural optimisation of dedicated chips while offering FPGA-like universal configurability. Central to this parallel multiprocessor is an asynchronous event-driven model that uses interrupt-generating dedicated hardware on the chip to support real-time neural simulation. While this architecture is particularly suitable for spiking models, it can also implement "classical" neural models like the MLP efficiently. Nonetheless, event handling, particularly servicing incoming packets, requires careful and innovative design in order to avoid local processor congestion and possible deadlock. Using two exemplar models, a spiking network using Izhikevich neurons, and an MLP network, we illustrate how to implement efficient service routines to handle input events. These routines form the beginnings of a library of "drop-in" neural components. Ultimately, the goal is the creation of a library-based development system that allows the modeller to describe a model in a high-level neural description environment of his choice and use an automated tool chain to create the appropriate SpiNNaker instantiation. The complete system: universal hardware, automated tool chain, embedded system management, represents the "ideal" neural modelling environment: a general-purpose platform that can generate an arbitrary neural network and run it with hardware speed and scale.