ADEE-LID: Automated Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers

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@InProceedings{Hurta:2023:DATE,

• author = "Martin Hurta and Vojtech Mrazek and Michaela Drahosova and Lukas Sekanina",
• booktitle = "2023 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)",
• title = "{ADEE-LID:} Automated Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers",
• year = "2023",
• 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.",
• keywords = "genetic algorithms, genetic programming, EHW, Energy consumption, Wearable computers, Estimation, Medical services, Feature extraction, levodopa-induced dyskinesia, energy efficiency, hardware accelerator",
• DOI = "doi:10.23919/DATE56975.2023.10137079",
• ISSN = "1558-1101",
• month = apr,
• notes = "Also known as \cite{10137079}",

}

Genetic Programming entries for Martin Hurta Vojtech Mrazek Michaela Sikulova Lukas Sekanina

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