MODEE-LID: Multiobjective Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers

MODEE-LID: Multiobjective Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers

Paper in proceedings (conference paper)

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 %.

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 %.

levodopa-induced dyskinesia, energy efficient,
hardware accelerator, multiobjective design

levodopa-induced dyskinesia, energy efficient,
hardware accelerator, multiobjective design

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

2024

03.05.2023

Institute of Electrical and Electronics Engineers

Tallinn

979-8-3503-3277-3

2023 26th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)

155

160

6

https://ieeexplore.ieee.org/document/10139399