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Digital Fixed-Point Low Powered Area Efficient Function Estimation for Implantable Devices

dc.contributor.authorRomaine, James Brian
dc.contributor.authorAshley, Thomas Ian
dc.contributor.authorPereira Martín, Mario
dc.date.accessioned2024-02-07T09:32:05Z
dc.date.available2024-02-07T09:32:05Z
dc.date.issued2022-06-30
dc.identifier.citationB. James Romaine and M. P. Martín, "High-Throughput Low Power Area Efficient 17-bit 2’s Complement Multilayer Perceptron Components and Architecture for on-Chip Machine Learning in Implantable Devices," in IEEE Access, vol. 10, pp. 92516-92531, 2022, doi: 10.1109/ACCESS.2022.320317es
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/20.500.12412/5074
dc.description.abstractThis article introduces a new multiplier-less 32-bit fixed point architecture for estimating complex non-linear functions based on adapted shift only series expansions. This novel hardware structure has been proposed for use as a dedicated core unit in implantable medical devices. Its implementation in FPGA produces a mean squared error of 0.23% over the functions sin(x),cos(x),eix and tan−1(x) when compared to unrestricted CPU implementations. These results are achieved with the use of only 133 sliced registers and 399 Look-up-tables (LUTs). Furthermore, the hardware performs extremely well in our hardware-in-the-loop real use case application for the detection of epilepsy by correctly detecting true positive seizures. When implemented into 130 nm technology via GOOGLE Sky130 PDK and Openlane EDA tools, the ASIC occupies a space of 0.0625 mm2 which represents a 47% reduction when compared to competitors. In addition, its power consumption is reduced to 6.46 mW at 100 MHz fo and just 0.4 μW at 1KHz fo .es
dc.description.sponsorshipUniversidad Loyola Andaluciaes
dc.language.isoenges
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleDigital Fixed-Point Low Powered Area Efficient Function Estimation for Implantable Deviceses
dc.typearticlees
dc.identifier.doi10.1109/ACCESS.2022.3187439
dc.journal.titleIEEE Accesses
dc.page.initial70793es
dc.page.final70805es
dc.rights.accessRightsopenAccesses
dc.subject.keywordHardwarees
dc.subject.keywordPower demandes
dc.subject.keywordField programmable gate arrayses
dc.subject.keywordEstimationes
dc.subject.keywordEpilepsyes
dc.subject.keywordImplantses
dc.subject.keywordTable lookupes
dc.volume.number10es


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional