%0 Generic
%A Friedmann, Simon
%D 2013
%F heidok:15359
%K Neuromorphic engineering, Belohnungslernen, STDP
%R 10.11588/heidok.00015359
%T A New Approach to Learning in Neuromorphic Hardware
%U https://archiv.ub.uni-heidelberg.de/volltextserver/15359/
%X This thesis presents a novel, highly flexible approach to plasticity and learning in  brain-inspired computing systems. A classical digital processor was combined with  local analog processing to achieve flexibility and efficiency. In particular, this allows  for the implementation of modulated spike-timing dependent plasticity. The approach  was formalized into an abstract hybrid hardware model. This model was used to  simulate a reward-based learning task to estimate the effect of hardware constraints.  To investigate the feasibility of the proposed architecture, a synthesizeable plasticity  processor was designed and tested using the CoreMark general purpose benchmark  (best score: 1.89 per MHz). The processor was also produced as part of a 65 nm proto-  type chip, requiring 0.14 mm2 of die-area, and reaching a maximum clock frequency of  769 MHz. In a preparatory step a non-programmable plasticity implementation was  developed, that is now part of the operational BrainScaleS wafer-scale system. This  design was later extended with the plasticity processor to implement the proposed  hybrid architecture. Simulations show a speed improvement of 42 % over the non-  programmable variant. By preparation for production, the area requirement for the  digital part is estimated to be 6.2 % of total area.