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FIT, DCSY – Researcher
+420 54114 1349ipinos@fit.vut.cz
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2024
PIŇOS, M.; SEKANINA, L.; MRÁZEK, V. ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers. The International Joint Conference on Neural Networks. 2024. p. 0-0. Detail | WWW
2023
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 | WWW
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 | WWW
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 | WWW
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 | WWW
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 | WWW
*) Publications are generated once a 24 hours.