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Spike-based analog-digital neuromorphic information processing system for sensor applications
dc.contributor.author | Sánchez Rivera, Giovanny |
dc.contributor.author | Koickal, Thomas Jacob |
dc.contributor.author | Sripad T A, Athul |
dc.contributor.author | Gouveia, Luiz Carlos |
dc.contributor.author | Hamilton, Alister |
dc.contributor.author | Madrenas Boadas, Jordi |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica |
dc.date.accessioned | 2013-09-27T16:24:43Z |
dc.date.created | 2013 |
dc.date.issued | 2013 |
dc.identifier.citation | Sanchez , G. [et al.]. Spike-based analog-digital neuromorphic information processing system for sensor applications. A: IEEE International Symposium on Circuits and Systems. "2013 IEEE International Symposium on Circuits and Systems (ISCAS 2013) : Beijing, China, 19 - 23 May 2013". Beijing-Pekín: Institute of Electrical and Electronics Engineers (IEEE), 2013, p. 1624-1627. |
dc.identifier.isbn | 978-1-4673-5760-9 |
dc.identifier.uri | http://hdl.handle.net/2117/20227 |
dc.description.abstract | A spiking-neuron-based system that combines analog and digital multi-processor implementations for the bio-inspired processing of sensors is reported. This combination allows creating a powerful bio-inspired multiple-input sensor processing system for environment perception applications. The analog front-end encodes the input signal in a signed spike representation, which is further processed by means of a digital Spiking Neural Network (SNN) on a Single-Instruction Multiple-Data (SIMD) multiprocessor. The spike distribution for both systems is based on Address-Event Representation (AER) scheme, asynchronous for the Analog Pre-Processor (APP) and synchronous for the Digital Multi-Processor (DMP), synchronized by means of an AER transceiver. A proof-of-concept application of the system being able to process sensory information has been demonstrated. The system utilizes 30-neurons emulated by the DMP to process spike-encoded information provided by its analog counterpart, enabling the feature extraction of the input signal. The frequency detection capability of the system is experimentally reported. |
dc.format.extent | 4 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura::Sensors i actuadors |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Programació |
dc.subject.lcsh | Parallel processing (Electronic computers) |
dc.subject.other | Encoding |
dc.subject.other | Multiprocessing systems |
dc.subject.other | Neural nets |
dc.subject.other | Parallel processing |
dc.subject.other | Sensor fusion |
dc.title | Spike-based analog-digital neuromorphic information processing system for sensor applications |
dc.type | Conference report |
dc.subject.lemac | Processament en paral·lel (Ordinadors) |
dc.contributor.group | Universitat Politècnica de Catalunya. AHA - Arquitectures Hardware Avançades |
dc.identifier.doi | 10.1109/ISCAS.2013.6572173 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6572173&tag=1 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 12743740 |
dc.description.version | Postprint (published version) |
dc.date.lift | 10000-01-01 |
local.citation.author | Sanchez , G.; Koickal, T.; Sripad T A, A.; Gouveia, L.; Hamilton, A.; Madrenas, J. |
local.citation.contributor | IEEE International Symposium on Circuits and Systems |
local.citation.pubplace | Beijing-Pekín |
local.citation.publicationName | 2013 IEEE International Symposium on Circuits and Systems (ISCAS 2013) : Beijing, China, 19 - 23 May 2013 |
local.citation.startingPage | 1624 |
local.citation.endingPage | 1627 |