1 research outputs found
An Intrinsic Method for Fast Parameter Update on the SpiNNaker Platform
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.European Union 644096 “ECOMODE"European Union 687299 “NEURAM3”European Union FP7-604102Ministerio de Economía y Competitividad TEC2015-63884-C2-1-