Ing.

Michal Piňos

Ph.D. student

+420 54114 1349
ipinos@fit.vut.cz
L307 Office
175170/BUT personal ID

Publications

  • 2023

    PIŇOS, M.; MRÁZEK, V.; SEKANINA, L. Prediction of Inference Energy on CNN Accelerators Supporting Approximate Circuits. In 2023 26th International Symposium on Design and Diagnostics of Electronic Circuits and Systems. Talinn: Institute of Electrical and Electronics Engineers, 2023. p. 45-50. ISBN: 979-8-3503-3277-3. 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

    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

    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

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