Douthwaite, Matthew
Garcıa-Redondo, Fernando
Georgiou, Pantelis
Das, Shidhartha
2019-10-19
<p>Flexible electronics is becoming more prevalent in a wide range of applications, particularly wearable biomedical<br>
devices. These devices would greatly benefit from in-built intelligence allowing them to process data and identify features,<br>
in order to reduce transmission and power requirements. In this work, we present a novel time-domain multiply-accumulate<br>
(MAC) engine architecture that can act as the basic block of an artificial analogue neural network. The design does not require<br>
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.</p>
https://doi.org/10.1109/BIOCAS.2019.8919190
oai:zenodo.org:3741945
eng
Zenodo
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
BioCAS, 2019 IEEE Biomedical Circuits and Systems Conference, Nara, Japan, 17-19 October 2019
Flexible Electronics
MAC Operation
Neural Networks
Analogue Signal Processing
Wearable Sensors
A Time-Domain Current-Mode MAC Engine for Analogue Neural Networks in Flexible Electronics
info:eu-repo/semantics/conferencePaper