October 19, 2019 Conference paper Open Access

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

DCAT Export

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        <foaf:name>Georgiou, Pantelis</foaf:name>
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        <foaf:name>Das, Shidhartha</foaf:name>
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    <dct:title>A Time-Domain Current-Mode MAC Engine for Analogue Neural Networks in Flexible Electronics</dct:title>
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    <dcat:keyword>Flexible Electronics</dcat:keyword>
    <dcat:keyword>MAC Operation</dcat:keyword>
    <dcat:keyword>Neural Networks</dcat:keyword>
    <dcat:keyword>Analogue Signal Processing</dcat:keyword>
    <dcat:keyword>Wearable Sensors</dcat:keyword>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-10-19</dct:issued>
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    <dct:description>&lt;p&gt;Flexible electronics is becoming more prevalent in a wide range of applications, particularly wearable biomedical&lt;br&gt; devices. These devices would greatly benefit from in-built intelligence allowing them to process data and identify features,&lt;br&gt; in order to reduce transmission and power requirements. In this work, we present a novel time-domain multiply-accumulate&lt;br&gt; (MAC) engine architecture that can act as the basic block of an artificial analogue neural network. The design does not require&lt;br&gt; 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.&lt;/p&gt;</dct:description>
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    <dct:title>Computation-in-memory architecture based on resistive devices</dct:title>
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