Project Details

Automatizovaný návrh hardwarových akcelerátorů pro strojového učení zohledňující výpočetní zdroje

Project Period: 1. 1. 2021 – 31. 12. 2023

Project Type: grant

Code: GA21-13001S

Agency: Czech Science Foundation

Program: Standardní projekty

English title
Automated design of hardware accelerators for resource-aware machine learning
Type
grant
Keywords

evolutionary algorithm, deep neural network, machine learning, accelerator, digital circuit, power consumption, evolvable hardware

Abstract

Machine learning (ML), particularly the technology based on deep neural networks (DNNs), has already reached and overcome human-level capabilities in many domains. A significant future use of trained ML models is expected in battery powered devices, where the major constraints are energy and the amount of resources available on a chip. The current approach to the DNN design is based on semi-automated simplifying of a network which is created by a human expert who could only partly reflect all hardware implementation aspects. In this project, the aim is to propose and evaluate a methodology for the highly automated design of hardware accelerators of DNNs (and other selected ML methods) that show excellent trade-offs between the output quality, energy and resources used on a single chip. Our approach is based on evolutionary design of such implementations of DNNs (and other ML systems) that reflect a target hardware platform. The proposed method will be evaluated on standard benchmark problems such as image classification and on automated assessment of Parkinsons disease.

Team members
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY) – research leader
Drahošová Michaela, Ing., Ph.D. (DCSY)
Hurta Martin, Ing. (DCSY)
Matoušek Jiří, Ing., Ph.D. (DCSY)
Mrázek Vojtěch, Ing., Ph.D. (DCSY)
Piňos Michal, Ing. (DCSY)
Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY)
Žufan Petr, Ing.
Publications

2024

  • HUSA, J.; SEKANINA, L. Semantic Mutation Operator for Fast and Efficient Design of Bent Boolean Functions. Genetic Programming and Evolvable Machines, 2024, vol. 25, no. 3, p. 1-32. ISSN: 1389-2576. Detail

2023

  • CHLEBÍK, J.; JAROŠ, J. Evolutionary Optimization of a Focused Ultrasound Propagation Predictor Neural Network. GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion. Lisbon: Association for Computing Machinery, 2023. p. 635-638. ISBN: 979-8-4007-0120-7. Detail
  • GIACOBINI, M.; PAPPA, G.; VAŠÍČEK, Z. Genetic Programming. LNCS 13986. Cham: Springer Verlag, 2023. p. 0-0. ISBN: 978-3-031-29572-0. Detail
  • HURTA, M.; MRÁZEK, V.; DRAHOŠOVÁ, M.; SEKANINA, L. Multi-objective Design of Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers. Evo* 2023 -- Late-Breaking Abstracts Volume. Brno: 2023. p. 0-0. Detail
  • HURTA, M.; MRÁZEK, V.; DRAHOŠOVÁ, M.; SEKANINA, L. ADEE-LID: Automated Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers. In 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). Antwerp: Institute of Electrical and Electronics Engineers, 2023. p. 1-2. ISBN: 978-3-9819263-7-8. Detail
  • HURTA, M.; MRÁZEK, V.; DRAHOŠOVÁ, M.; SEKANINA, L. MODEE-LID: Multiobjective Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers. In 2023 26th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS). Tallinn: Institute of Electrical and Electronics Engineers, 2023. p. 155-160. ISBN: 979-8-3503-3277-3. Detail
  • HUSA, J.; SEKANINA, L. Semantic Mutation Operator for Fast and Efficient Design of Bent Boolean Functions. Evo* 2023 -- Late-Breaking Abstracts Volume. Brno: 2023. p. 0-0. Detail
  • JŮZA, T.; SEKANINA, L. GPAM: Genetic Programming with Associative Memory. In 26th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar. Lecture Notes in Computer Science. LNCS. Cham: Springer Nature Switzerland AG, 2023. p. 68-83. ISBN: 978-3-031-29572-0. ISSN: 0302-9743. Detail
  • LOJDA, J.; PÁNEK, R.; SEKANINA, L.; KOTÁSEK, Z. Automated Design and Usage of the Fault-Tolerant Dynamic Partial Reconfiguration Controller for FPGAs. Microelectronics Reliability, 2023, vol. 2023, no. 144, p. 1-16. ISSN: 0026-2714. Detail
  • MRÁZEK, V. Approximation of Hardware Accelerators driven by Machine-Learning Models : (Embedded Tutorial). In Proceedings of International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS '23). Tallinn: Institute of Electrical and Electronics Engineers, 2023. p. 91-92. ISBN: 979-8-3503-3277-3. Detail
  • MRÁZEK, V.; JAWED, S.; ARIF, M.; MALIK, A. Effective EEG Feature Selection for Interpretable MDD (Major Depressive Disorder) Classification. In GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference. Lisbon: Association for Computing Machinery, 2023. p. 1427-1435. ISBN: 979-8-4007-0119-1. Detail
  • PIŇOS, M.; MRÁZEK, V.; VAVERKA, F.; VAŠÍČEK, Z.; SEKANINA, L. Acceleration Techniques for Automated Design of Approximate Convolutional Neural Networks. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2023, vol. 13, no. 1, p. 212-224. ISSN: 2156-3357. Detail
  • PRABAKARAN, B.; MRÁZEK, V.; VAŠÍČEK, Z.; SEKANINA, L.; SHAFIQUE, M. Xel-FPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems. In 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD). San Francisco: Institute of Electrical and Electronics Engineers, 2023. p. 1-9. ISBN: 979-8-3503-1559-2. Detail
  • QADRI, S.; ARIF, M.; SAEED, M. A Novel Variable Step-Size LMS Algorithm for Decentralized Incremental Distributed Networks. CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2023, vol. 41, no. 12, p. 7226-7249. ISSN: 0278-081X. Detail
  • SEKANINA, L.; MRÁZEK, V.; PIŇOS, M. Hardware-Aware Evolutionary Approaches to Deep Neural Networks. In Handbook of Evolutionary Machine Learning. Genetic and Evolutionary Computation. Singapore: Springer Nature Singapore, 2023. p. 367-396. ISBN: 978-981-9938-13-1. Detail

2022

  • BOSIO, A.; DI CARLO, S.; GIRARD, P.; RUOSPO, A.; SANCHEZ, E.; SAVINO, A.; SEKANINA, L.; TRAIOLA, M.; VAŠÍČEK, Z.; VIRAZEL, A. Design, Verification, Test, and In-Field Implications of Approximate Digital Integrated Circuits. In Approximate Computing Techniques. Cham: Springer International Publishing, 2022. p. 349-385. ISBN: 978-3-030-94704-0. Detail
  • HANIF, M.; MRÁZEK, V.; SHAFIQUE, M. Approximate Computing Architectures. In Handbook of Computer Architecture. Handbook of Computer Architecture. Singapore: Springer Nature Singapore, 2022. p. 1-41. ISBN: 978-981-1564-01-7. Detail
  • HURTA, M.; DRAHOŠOVÁ, M.; MRÁZEK, V. Evolutionary Design of Reduced Precision Preprocessor for Levodopa-Induced Dyskinesia Classifier. In Parallel Problem Solving from Nature - PPSN XVII. Lecture Notes in Computer Science. Dortmund: Springer Nature Switzerland AG, 2022. p. 491-504. ISBN: 978-3-031-14713-5. Detail
  • HURTA, M.; DRAHOŠOVÁ, M.; SEKANINA, L.; SMITH, S.; ALTY, J. Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers. In Genetic Programming, 25th European Conference, EuroGP 2022. Lecture Notes in Computer Science. Madrid: Springer Nature Switzerland AG, 2022. p. 85-101. ISBN: 978-3-031-02055-1. Detail
  • PIŇOS, M.; MRÁZEK, V.; SEKANINA, L. Evolutionary Approximation and Neural Architecture Search. Genetic Programming and Evolvable Machines, 2022, vol. 23, no. 3, p. 351-374. ISSN: 1389-2576. Detail

2021

  • PIŇOS, M.; MRÁZEK, V.; SEKANINA, L. Evolutionary Neural Architecture Search Supporting Approximate Multipliers. In Genetic Programming, 24th European Conference, EuroGP 2021. Lecture Notes in Computer Science, vol 12691. Seville: Springer Nature Switzerland AG, 2021. p. 82-97. ISBN: 978-3-030-72812-0. Detail
  • SEKANINA, L. Evolutionary Algorithms in Approximate Computing: A Survey. Journal of Integrated Circuits and Systems, 2021, vol. 16, no. 2, p. 1-12. ISSN: 1872-0234. Detail
  • SEKANINA, L. Neural Architecture Search and Hardware Accelerator Co-Search: A Survey. IEEE Access, 2021, vol. 9, no. 9, p. 151337-151362. ISSN: 2169-3536. Detail
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