Publication result detail

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

HURTA, M.; MRÁZEK, V.; DRAHOŠOVÁ, M.; SEKANINA, L.

Original Title

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

English Title

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

Type

Paper in proceedings (conference paper)

Original 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.

English 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

levodopa-induced dyskinesia, energy efficient,
hardware accelerator, genetic programming

Key words in English

levodopa-induced dyskinesia, energy efficient,
hardware accelerator, genetic programming

Authors

HURTA, M.; MRÁZEK, V.; DRAHOŠOVÁ, M.; SEKANINA, L.

RIV year

2024

Released

17.04.2023

Publisher

Institute of Electrical and Electronics Engineers

Location

Antwerp

ISBN

978-3-9819263-7-8

Book

2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)

Pages from

1

Pages to

2

Pages count

2

URL

https://ieeexplore.ieee.org/document/10137079

BibTex

@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"
}