October 19, 2019 Conference paper Open Access

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

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    "description": "<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>", 
    "language": "eng", 
    "title": "A Time-Domain Current-Mode MAC Engine for Analogue Neural Networks in Flexible Electronics", 
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        "title": "Computation-in-memory architecture based on resistive devices", 
        "acronym": "MNEMOSENE", 
        "program": "H2020", 
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          "name": "European Commission", 
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    "keywords": [
      "Flexible Electronics", 
      "MAC Operation", 
      "Neural Networks", 
      "Analogue Signal Processing", 
      "Wearable Sensors"
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    "publication_date": "2019-10-19", 
    "creators": [
      {
        "affiliation": "Imperial College London", 
        "name": "Douthwaite, Matthew"
      }, 
      {
        "affiliation": "ARM Research", 
        "name": "Garc\u0131a-Redondo, Fernando"
      }, 
      {
        "affiliation": "Imperial College London", 
        "name": "Georgiou, Pantelis"
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      {
        "affiliation": "ARM Research", 
        "name": "Das, Shidhartha"
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    "meeting": {
      "acronym": "BioCAS", 
      "dates": "17-19 October 2019", 
      "place": "Nara, Japan", 
      "title": "2019 IEEE Biomedical Circuits and Systems Conference"
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