ADEE-LID: Automated Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Hurta:2023:DATE,
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author = "Martin Hurta and Vojtech Mrazek and
Michaela Drahosova and Lukas Sekanina",
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booktitle = "2023 Design, Automation \& Test in Europe Conference
\& Exhibition (DATE)",
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title = "{ADEE-LID:} Automated Design of Energy-Efficient
Hardware Accelerators for Levodopa-Induced Dyskinesia
Classifiers",
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year = "2023",
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abstract = "Levodopa, a drug used to treat symptoms of Parkinson's
disease, is connected to side effects known as
Levodopa-induced dyskinesia (LID). LID is difficult to
classify during a physician's visit. A wearable device
allowing long-term and continuous classification would
significantly help with dosage adjustments. This paper
deals with an automated design of energy-efficient
hardware accelerators for such LID classifiers. The
proposed accelerator consists of a feature extractor
and a classifier co-designed using genetic programming.
Improvements are achieved by introducing a variable bit
width for arithmetic operators, eliminating redundant
registers, and using precise energy consumption
estimation for Pareto front creation. Evolved solutions
reduce energy consumption while maintaining
classification accuracy comparable to the state of the
art.",
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keywords = "genetic algorithms, genetic programming, EHW, Energy
consumption, Wearable computers, Estimation, Medical
services, Feature extraction, levodopa-induced
dyskinesia, energy efficiency, hardware accelerator",
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DOI = "doi:10.23919/DATE56975.2023.10137079",
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ISSN = "1558-1101",
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month = apr,
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notes = "Also known as \cite{10137079}",
- }
Genetic Programming entries for
Martin Hurta
Vojtech Mrazek
Michaela Sikulova
Lukas Sekanina
Citations