Publication Details
ADEE-LID: Automated Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers
Mrázek Vojtěch, Ing., Ph.D. (DCSY)
Drahošová Michaela, Ing., Ph.D. (DCSY)
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
levodopa-induced dyskinesia, energy efficient,
hardware accelerator, genetic programming
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.
@inproceedings{BUT184452,
author="Martin {Hurta} and Vojtěch {Mrázek} and Michaela {Drahošová} and Lukáš {Sekanina}",
title="ADEE-LID: Automated Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers",
booktitle="2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)",
year="2023",
pages="1--2",
publisher="Institute of Electrical and Electronics Engineers",
address="Antwerp",
doi="10.23919/DATE56975.2023.10137079",
isbn="978-3-9819263-7-8",
url="https://ieeexplore.ieee.org/document/10137079"
}