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HURTA, M. MRÁZEK, V. DRAHOŠOVÁ, M. SEKANINA, L.
Original Title
Multi-objective Design of Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers
Type
abstract
Language
English
Original Abstract
Taking levodopa, a drug used to treat symptoms of Parkinson's disease, is often connected with severe side effects, known as Levodopa-induced dyskinesia (LID). It can fluctuate in severity throughout the day and thus is difficult to classify during a short period of a physician's visit. A low-power wearable classifier enabling long-term and continuous LID classification would thus significantly help with LID detection and dosage adjustment. This paper deals with a co-evolutionary design of energy-efficient hardware accelerators of LID classifiers that can be implemented in wearable devices. The accelerator consists of a feature extractor and a classifier co-evolved using cartesian genetic programming (CGP). We introduce and evaluate a fast and accurate energy consumption estimation method for the target architecture of considered classifiers. The proposed energy estimation method allows for a multi-objective design enabled by introducing energy constraints. With the introduction of variable data representation bit width, the proposed method achieves a good trade-off between accuracy (AUC) and energy consumption.
Keywords
Cartesian genetic programming, Multi-objective design, Hardware accelerator, Energy-efficient, Levodopa-induced dyskinesia
Authors
HURTA, M.; MRÁZEK, V.; DRAHOŠOVÁ, M.; SEKANINA, L.
Released
12. 4. 2023
Location
Brno
Pages count
4
URL
https://arxiv.org/abs/2403.13950
BibTex
@misc{BUT185155, author="Martin {Hurta} and Vojtěch {Mrázek} and Michaela {Drahošová} and Lukáš {Sekanina}", title="Multi-objective Design of Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers", booktitle="Evo* 2023 -- Late-Breaking Abstracts Volume", year="2023", pages="4", address="Brno", doi="10.48550/arXiv.2403.13950", url="https://arxiv.org/abs/2403.13950", note="abstract" }