October 19, 2019 Conference paper Open Access

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

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    <subfield code="a">&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;</subfield>
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