An Intrinsic Method for Fast Parameter Update on the SpiNNaker Platform

Abstract

Neuromorphic Computing or Spiking (also called Event-Driven) Neural Systems are becoming of high interest as they potentially allow for lower power hardware computing platforms, where power consumption is data driven. Traditional approaches (both in software and in hardware), which are not data driven, rely on generic system state updates, consuming a fixed amount of computing resources at each step, independent on the data itself. In neuromorphic spiking or (event-driven) computing systems power is consumed (in principle) if new data is transferred, either at the system input, system output, or internally between computing nodes. One such neuromorphic event-driven computing platform is the scalable SpiNNaker system, which is aimed for a million ARM core platform, capable of emulating in the order of a billion neurons in real time. An important practical drawback of the platform is the long time it takes to download to the hardware a given computational architecture. This step has to be repeated even if one wants to update a set of parameters. Here we present a method for updating internal parameters without downloading again the full architecture, by adding special neurons into the computing architecture which when they spike change given parameters. This allows to download the computing architecture only once to the SpiNNaker platform, and then take advantage of its highly efficient communication network to command specific parameter changes. This allows for intensive parameter searches in a more efficient manner.