Cargando…
Configurable analog-digital conversion using the neural engineering framework
Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and dig...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4106401/ https://www.ncbi.nlm.nih.gov/pubmed/25100933 http://dx.doi.org/10.3389/fnins.2014.00201 |
_version_ | 1782327506839273472 |
---|---|
author | Mayr, Christian G. Partzsch, Johannes Noack, Marko Schüffny, Rene |
author_facet | Mayr, Christian G. Partzsch, Johannes Noack, Marko Schüffny, Rene |
author_sort | Mayr, Christian G. |
collection | PubMed |
description | Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e., to build neurally inspired ADCs. Generally, these have suffered from the same problems as conventional ADCs, that is they require high-precision, handcrafted analog circuits and are thus not technology portable. In this paper, we present an ADC based on the Neural Engineering Framework (NEF). It carries out a large fraction of the overall ADC process in the digital domain, i.e., it is easily portable across technologies. The analog-digital conversion takes full advantage of the high degree of parallelism inherent in neuromorphic networks, making for a very scalable ADC. In addition, it has a number of features not commonly found in conventional ADCs, such as a runtime reconfigurability of the ADC sampling rate, resolution and transfer characteristic. |
format | Online Article Text |
id | pubmed-4106401 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-41064012014-08-06 Configurable analog-digital conversion using the neural engineering framework Mayr, Christian G. Partzsch, Johannes Noack, Marko Schüffny, Rene Front Neurosci Neuroscience Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e., to build neurally inspired ADCs. Generally, these have suffered from the same problems as conventional ADCs, that is they require high-precision, handcrafted analog circuits and are thus not technology portable. In this paper, we present an ADC based on the Neural Engineering Framework (NEF). It carries out a large fraction of the overall ADC process in the digital domain, i.e., it is easily portable across technologies. The analog-digital conversion takes full advantage of the high degree of parallelism inherent in neuromorphic networks, making for a very scalable ADC. In addition, it has a number of features not commonly found in conventional ADCs, such as a runtime reconfigurability of the ADC sampling rate, resolution and transfer characteristic. Frontiers Media S.A. 2014-07-22 /pmc/articles/PMC4106401/ /pubmed/25100933 http://dx.doi.org/10.3389/fnins.2014.00201 Text en Copyright © 2014 Mayr, Partzsch, Noack and Schüffny. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Mayr, Christian G. Partzsch, Johannes Noack, Marko Schüffny, Rene Configurable analog-digital conversion using the neural engineering framework |
title | Configurable analog-digital conversion using the neural engineering framework |
title_full | Configurable analog-digital conversion using the neural engineering framework |
title_fullStr | Configurable analog-digital conversion using the neural engineering framework |
title_full_unstemmed | Configurable analog-digital conversion using the neural engineering framework |
title_short | Configurable analog-digital conversion using the neural engineering framework |
title_sort | configurable analog-digital conversion using the neural engineering framework |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4106401/ https://www.ncbi.nlm.nih.gov/pubmed/25100933 http://dx.doi.org/10.3389/fnins.2014.00201 |
work_keys_str_mv | AT mayrchristiang configurableanalogdigitalconversionusingtheneuralengineeringframework AT partzschjohannes configurableanalogdigitalconversionusingtheneuralengineeringframework AT noackmarko configurableanalogdigitalconversionusingtheneuralengineeringframework AT schuffnyrene configurableanalogdigitalconversionusingtheneuralengineeringframework |