Programmable plasticity on neuromorphic hardware
One of the essential properties of neurons and synapses is that they can change over time, in particular as a result of learning. In the newly developed HICANN-DLS chip from Heidelberg, we use an on-chip plasticity processing unit (NUX) to control that change. In this workshop we plan to implement small recurrent networks in which the NUX then modulates, for example, synaptic weights (or any other parameters of the running system).
The prototype chip features an array of 32 x 32 synapses, each of which stores a 6 Bit weight and analog correlation traces that relate pre- and postsynaptic events. The plasticity processor can use that information to implement various plasticity rules, with STDP being just one example. For this purpose, it has a vector unit with direct access to the analog and digital state of all synapses. The analog parameter storage used to configure and calibrate the neurons can also be controlled by the plasticity processor.
While the focus of the workshop will be on a tutorial-style introduction and subsequent experimentation with our prototype system, the workshop can also serve as a platform for a discussion of possible learning algorithms and implementations that are suitable for such a flexible device.
|Tue, 26.04.2016||14:00 - 15:00||Disco|