Publikace

• 2023

  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. s. 367-396. ISBN: 978-981-9938-13-1.
  Detail | WWW

  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, roč. 13, č. 1, s. 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. s. 45-50. ISBN: 979-8-3503-3277-3.
  Detail | WWW

• 2022

  PIŇOS, M.; MRÁZEK, V.; SEKANINA, L. Evolutionary Approximation and Neural Architecture Search. Genetic Programming and Evolvable Machines, 2022, roč. 23, č. 3, s. 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. s. 82-97. ISBN: 978-3-030-72812-0.
  Detail | WWW

*) Citace publikací se generují jednou za 24 hodin.