October 19, 2019 Conference paper Open Access

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

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{
  "DOI": "10.1109/BIOCAS.2019.8919190", 
  "language": "eng", 
  "title": "A Time-Domain Current-Mode MAC Engine for Analogue Neural Networks in Flexible Electronics", 
  "issued": {
    "date-parts": [
      [
        2019, 
        10, 
        19
      ]
    ]
  }, 
  "abstract": "<p>Flexible electronics is becoming more prevalent in a wide range of applications, particularly wearable biomedical<br>\ndevices. These devices would greatly benefit from in-built intelligence allowing them to process data and identify features,<br>\nin order to reduce transmission and power requirements. In this work, we present a novel time-domain multiply-accumulate<br>\n(MAC) engine architecture that can act as the basic block of an artificial analogue neural network. The design does not require<br>\nanalogue 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.</p>", 
  "author": [
    {
      "family": "Douthwaite, Matthew"
    }, 
    {
      "family": "Garc\u0131a-Redondo, Fernando"
    }, 
    {
      "family": "Georgiou, Pantelis"
    }, 
    {
      "family": "Das, Shidhartha"
    }
  ], 
  "id": "3741945", 
  "event-place": "Nara, Japan", 
  "type": "paper-conference", 
  "event": "2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)"
}