Zobrazeno 1 - 2
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pro vyhledávání: '"Sepide Saeedi"'
Prediction of the Impact of Approximate Computing on Spiking Neural Networks via Interval Arithmetic
Publikováno v:
2022 IEEE 23rd Latin American Test Symposium (LATS)
Approximate Computing (AxC) techniques allow trade-off accuracy for performance, energy, and area reduction gains. One of the applications suitable for using AxC techniques are the Spiking Neural Networks (SNNs). SNNs are the new frontier for artific
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bff6a5af430a57e32061b2a690115764
https://doi.org/10.1109/lats57337.2022.9936999
https://doi.org/10.1109/lats57337.2022.9936999
Autor:
Ali Piri, Sepide Saeedi, Mario Barbareschi, Bastien Deveautour, Stefano Di Carlo, Ian O'Connor, Alessandro Savino, Marcello Traiola, Alberto Bosio
Publikováno v:
AQTR 2022-IEEE International Conference on Automation, Quality and Testing, Robotics
AQTR 2022-IEEE International Conference on Automation, Quality and Testing, Robotics, May 2022, Cluj-Napoca, Romania. pp.1-6, ⟨10.1109/AQTR55203.2022.9801944⟩
2022 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)
AQTR 2022-IEEE International Conference on Automation, Quality and Testing, Robotics, May 2022, Cluj-Napoca, Romania. pp.1-6, ⟨10.1109/AQTR55203.2022.9801944⟩
2022 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)
International audience; In the last decade, Approximate Computing (AxC) has been extensively employed to improve the energy efficiency of computing systems, at different abstraction levels. The main AxC goal is reducing the energy budget used to exec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df9b54ecb33ad77d8b59f34caf41af7e
https://hdl.handle.net/11588/915830
https://hdl.handle.net/11588/915830
