SpinLink: An interconnection system for the SpiNNaker biologically inspired multi-computer

SpiNNaker is a large-scale biologically-inspired multi-computer designed to model very heavily distributed problems, with the flagship application being the simulation of large neural networks. The project goal is to have one million processors included in a single machine, which consequently span many thousands of circuit boards. A computer of this scale imposes large communication requirements between these boards, and requires an extensible method of connecting to external equipment such as sensors, actuators and visualisation systems. This paper describes two systems that can address each of these problems.Firstly, SpinLink is a proposed method of connecting the SpiNNaker boards by using time-division multiplexing (TDM) to allow eight SpiNNaker links to run at maximum bandwidth between two boards. SpinLink will be deployed on Spartan-6 FPGAs and uses a locally generated clock that can be paused while the asynchronous links from SpiNNaker are sending data, thus ensuring a fast and glitch-free response. Secondly, SpiNNterceptor is a separate system, currently in the early stages of design, that will build upon SpinLink to address the important external I/O issues faced by SpiNNaker. Specifically, spare resources in the FPGAs will be used to implement the debugging and I/O interfacing features of SpiNNterceptor

Reliable computation with unreliable computers

As computing systems continue their unquenchable rise towards and through million core architectures, two considerations that used to be unimportant become more and more dominant: power consumption (be it FLOPS/W or W/mm2) and reliability. This study is concerned with the latter: in a system of a million cores, it is unrealistic to expect 100% functionality on power-up; equally, operational availability degrades with time. Monitoring and maintaining the health of such a system using traditional techniques is costly, and most rely on the concept of some sort of central overseer or monitor to make a final judgement about system availability, giving a single point of failure. Large systems of the future will consist of hardware and software that work synergistically to cope with isolated points of failure, allowing the gross behaviour of the system to degrade gracefully and in a meaningful way in the face of faults. This study describes one such system: spiking neural network architecture is a million-core machine with layered fault tolerance built in at many levels. The authors show how the system may be used to solve the canonical distributed heat diffusion equation, and how the quality of solution is modulated by the effects of partial system failure

SpiNNaker: Event-Based Simulation—Quantitative Behavior:Event-based simulation - quantitative behaviour

SpiNNaker (Spiking Neural Network Architecture) is a specialized computing engine, intended for real-time simulation of neural systems. It consists of a mesh of 240x240 nodes, each containing 18 ARM9 processors: over a million cores, communicating via a bespoke network. Ultimately, the machine will support the simulation of up to a billion neurons in real time, allowing simulation experiments to be taken to hitherto unattainable scales. The architecture achieves this by ignoring three of the axioms of computer design: the communication fabric is non-deterministic; there is no global core synchronisation, and the system state—held in distributed memory—is not coherent. Time models itself: there is no notion of computed simulation time—wallclock time is simulation time. Whilst these design decisions are orthogonal to conventional wisdom, they bring the engine behavior closer to its intended simulation target—neural systems. We describe how SpiNNaker simulates large neural ensembles; we provide performance figures and outline some failure mechanisms. SpiNNaker simulation time scales 1:1 with wallclock time at least up to nine million synaptic connections on a 768 core subsystem (�1400th of the full system) to accurately produce logically predicted results