MODEE-LID: Multiobjective Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU148869" target="_blank" >RIV/00216305:26230/23:PU148869 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10139399" target="_blank" >https://ieeexplore.ieee.org/document/10139399</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/DDECS57882.2023.10139399" target="_blank" >10.1109/DDECS57882.2023.10139399</a>
Alternative languages
Result language
angličtina
Original language name
MODEE-LID: Multiobjective Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers
Original language description
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 an automated 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 classification circuit co-designed using genetic programming (GP). We also introduce and evaluate a fast and accurate energy consumption estimation method for the target architecture of considered classifiers. In a multiobjective design scenario, GP evolves solutions showing the best trade-offs between accuracy and energy. Compared to the state-of-the-art solutions, the proposed method leads to classifiers showing a comparable accuracy while the energy consumption is reduced by 49 %.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA21-13001S" target="_blank" >GA21-13001S: Automated design of hardware accelerators for resource-aware machine learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2023 26th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)
ISBN
979-8-3503-3277-3
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
155-160
Publisher name
Institute of Electrical and Electronics Engineers
Place of publication
Tallinn
Event location
Tallinn
Event date
May 3, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001012062000030