October 19, 2019 Conference paper Open Access

Douthwaite, Matthew; Garcıa-Redondo, Fernando; Georgiou, Pantelis; Das, Shidhartha

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  <dc:creator>Douthwaite, Matthew</dc:creator>
  <dc:creator>Garcıa-Redondo, Fernando</dc:creator>
  <dc:creator>Georgiou, Pantelis</dc:creator>
  <dc:creator>Das, Shidhartha</dc:creator>
  <dc:date>2019-10-19</dc:date>
  <dc:description>Flexible electronics is becoming more prevalent in a wide range of applications, particularly wearable biomedical
devices. These devices would greatly benefit from in-built intelligence allowing them to process data and identify features,
in order to reduce transmission and power requirements. In this work, we present a novel time-domain multiply-accumulate
(MAC) engine architecture that can act as the basic block of an artificial analogue neural network. The design does not require
analogue voltage buffers, making them easier to realise in flexible technologies and consumes less power than conventional methods. The research could be used in future to construct a low power classifier for a low cost, flexible wearable biomedical sensor.</dc:description>
  <dc:identifier>https://zenodo.org/record/3741945</dc:identifier>
  <dc:identifier>10.1109/BIOCAS.2019.8919190</dc:identifier>
  <dc:identifier>oai:zenodo.org:3741945</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/780215/</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Flexible Electronics</dc:subject>
  <dc:subject>MAC Operation</dc:subject>
  <dc:subject>Neural Networks</dc:subject>
  <dc:subject>Analogue Signal Processing</dc:subject>
  <dc:subject>Wearable Sensors</dc:subject>
  <dc:title>A Time-Domain Current-Mode MAC Engine for Analogue Neural Networks in Flexible Electronics</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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