Evolutionary Design of Reduced Precision Preprocessor for Levodopa-Induced Dyskinesia Classifier

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU145297" target="_blank" >RIV/00216305:26230/22:PU145297 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-14714-2_34" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-14714-2_34</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-14714-2_34" target="_blank" >10.1007/978-3-031-14714-2_34</a>

Result language

angličtina

Original language name

Evolutionary Design of Reduced Precision Preprocessor for Levodopa-Induced Dyskinesia Classifier

Original language description

The aim of this work is to design a hardware-efficient implementation of data preprocessing in the task of levodopa-induced dyskinesia classification. In this task, there are three approaches implemented and compared: 1) evolution of magnitude approximation using Cartesian genetic programming, 2) design of preprocessing unit using two-population coevolution (2P-CoEA) of cartesian programs and fitness predictors, which are small subsets of training set, and 3) a design using three-population coevolution (3P-CoEA) combining compositional coevolution of preprocessor and classifier with coevolution of fitness predictors. Experimental results show that all of the three investigated approaches are capable of producing energy-saving solutions, suitable for implementation in hardware unit, with a quality comparable to baseline software implementation. Design of approximate magnitude leads to correctly working solutions, however, more energy-demanding than other investigated approaches. 3P-CoEA is capable of designing both preprocessor and classifier compositionally while achieving smaller solutions than the design of approximate magnitude. Presented 2P-CoEA results in the smallest and the most energy-efficient solutions along with producing a solution with significantly better classification quality for one part of test data in comparison with the software implementation.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Parallel Problem Solving from Nature - PPSN XVII

ISBN

978-3-031-14713-5

ISSN

—

e-ISSN

—

Number of pages

14

Pages from-to

491-504

Publisher name

Springer Nature Switzerland AG

Place of publication

Dortmund

Event location

Dortmund, Germany

Event date

Sep 10, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000871752100034