eprintid: 17190
rev_number: 11
eprint_status: archive
userid: 1299
dir: disk0/00/01/71/90
datestamp: 2014-07-28 12:02:29
lastmod: 2014-08-07 07:52:01
status_changed: 2014-07-28 12:02:29
type: doctoralThesis
metadata_visibility: show
creators_name: Jeltsch, Sebastian
title: A Scalable Workflow for a Configurable
Neuromorphic Platform
divisions: i-130700
adv_faculty: af-13
cterms_swd: Gehirn
cterms_swd: Hardware
cterms_swd: Computer
abstract: This thesis establishes a scalable multi-user workflow for the operation of a highly configurable,
large-scale neuromorphic hardware platform. The resulting software framework provides unified
low-level as well as parallel high-level access. The latter is realized by an efficient abstract
neural network description library, an automated translation of networks into hardware specific
configurations and an experiment server infrastructure responsible for scheduling and executing
experiments. Scalability, manual guidance and a broad support for handling hardware imper-
fections render the model translation process suitable for large networks as well as large-scale
neuromorphic systems. Networks with local connectivity, random networks and cortical column
models are explored to study the topological aptitude of the neuromorphic platform and to
benchmark the workflow. Depending on the model, performance improvements of more than
two orders of magnitude have been achieved over a previous implementation. Additionally, an
automated defect assessment for hardware synapses is introduced, indicating that most synapses
are available for model emulation.
In a second study, a tempotron-based hardware liquid state machine has been developed
and applied to different tasks, including a memory challenge and digit recognition. The trained
tempotron inherently compensates for fixed pattern variations making the setup suitable for
analog neuromorphic hardware. The achieved performance is comparable to reference software
simulations.
date: 2014
id_scheme: DOI
id_number: 10.11588/heidok.00017190
ppn_swb: 1658773330
own_urn: urn:nbn:de:bsz:16-heidok-171902
date_accepted: 2014-07-23
advisor: HASH(0x55a99e4a1680)
language: eng
bibsort: JELTSCHSEBASCALABLEW2014
full_text_status: public
citation:   Jeltsch, Sebastian  (2014) A Scalable Workflow for a Configurable Neuromorphic Platform.  [Dissertation]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/17190/1/dissertation_sebastian_jeltsch_300dpi.pdf