Interfacing Neuromorphic Hardware with Machine Learning Frameworks - A Review-Reference-Cited by-同舟云学术

Interfacing Neuromorphic Hardware with Machine Learning Frameworks - A Review

Published:2023-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 International Conference on Neuromorphic Systems
language:
Short-container-title:

Author:

Lohoff Jamie¹²^ORCID,Yu Zhenming¹²^ORCID,Finkbeiner Jan¹²^ORCID,Kaya Anil¹²^ORCID,Stewart Kenneth³¹^ORCID,Wai Lui Hin³^ORCID,Neftci Emre¹²^ORCID

Affiliation:

1. Peter-Grünberg-Institute, Forschungzentrum Jülich, Jülich, Northrhine-Westfalia, Germany

2. Department of Electrical Engineering, RWTH Aachen, Aachen, Northrhine-Westfalia, Germany

3. University of California Irvine, Irvine, California, United States

Funder

BMBF

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589737.3605967

Reference51 articles.

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2. TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip

3. Joao Ambrosi , Aayush Ankit , co-design for an analog-digital accelerator for machine learning ". In: 2018 IEEE International Conference on Rebooting Computing (ICRC). IEEE. 2018 , pp. 1 -- 13 . Joao Ambrosi, Aayush Ankit, et al. "Hardware-software co-design for an analog-digital accelerator for machine learning". In: 2018 IEEE International Conference on Rebooting Computing (ICRC). IEEE. 2018, pp. 1--13.

4. Aayush Ankit , Izzat El Hajj, et al . PANTHER : A Programmable Architecture for Neural Network Training Harnessing Energyefficient ReRAM. 2019 . arXiv: 1912.11516 [cs.DC]. Aayush Ankit, Izzat El Hajj, et al. PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energyefficient ReRAM. 2019. arXiv: 1912.11516 [cs.DC].

5. Aayush Ankit , Izzat El Hajj , : A programmable ultraefficient memristorbased accelerator for machine learning inference ". In: Proceedings of the TwentyFourth International Conference on Architectural Support for Programming Languages and Operating Systems. 2019 , p. 715731 . Aayush Ankit, Izzat El Hajj, et al. "PUMA: A programmable ultraefficient memristorbased accelerator for machine learning inference". In: Proceedings of the TwentyFourth International Conference on Architectural Support for Programming Languages and Operating Systems. 2019, p. 715731.

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