title: A New Approach to Learning in Neuromorphic Hardware
creator: Friedmann, Simon
subject: 530
subject: 530 Physics
subject: 570
subject: 570 Life sciences
subject: 600
subject: 600 Technology (Applied sciences)
subject: 620
subject: 620 Engineering and allied operations
description: 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.  
date: 2013
type: Dissertation
type: info:eu-repo/semantics/doctoralThesis
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/15359/1/Diss_Simon_Friedmann_2013_screen.pdf
identifier: DOI:10.11588/heidok.00015359
identifier: urn:nbn:de:bsz:16-heidok-153597
identifier:   Friedmann, Simon  (2013) A New Approach to Learning in Neuromorphic Hardware.  [Dissertation]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/15359/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng