Event-Driven Configuration of a Neural Network CMP System over a Homogeneous Interconnect Fabric

Abstract

Configuring a million-core parallel system at boot time is a difficult process when the system has neither specialised hardware support for the configuration process nor a preconfigured default state that puts it in operating condition. SpiNNaker is a parallel Chip Multiprocessor (CMP) system for neural network (NN) simulation. Where most large CMP systems feature a sideband network to complete the boot process, SpiNNaker has a single homogeneous network interconnect for both application inter-processor communications and system control functions such as boot load and run-time user-system interaction. This network improves fault tolerance and makes it easier to support dynamic run-time reconfiguration, however, it requires a boot process that is transaction-level compatible with the application’s communications model. Since SpiNNaker uses event-driven asynchronous communications throughout, theloader operates with purely local control: there is no global synchronisation, state information, or transition sequence. A novel two-stage “unfolding” boot-up process efficiently configures the SpiNNaker hardware and loads the application using a high-speed flood-fill technique with support for run-time re-configuration. SystemC simulation of a multi-CMP SpiNNaker system indicates an error-free CMP configuration time of 1.3ms, while a high-level simulation of a full-scale system (64K CMPs) indicates a mean application-loading time of ∼20ms (for a 100KB application), which is virtually independent of the sizeof the system. We verified the CMP configuration process with hardware-level Verilog simulation.