Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware-Reference-Cited by-同舟云学术

Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware

Published:2021 Issue: Volume: Page:1-14
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Kleyko Denis^ORCID,Frady Edward Paxon,Kheffache Mansour^ORCID,Osipov Evgeny^ORCID

Funder

Vetenskapsrådet

European Unions Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Individual Fellowship Grant

Defense Advanced Research Projects Agency DARPAs Virtual Intelligence Processing VIP Super-HD Project and the Artificial Intelligence Exploration AIE HyDDENN Project programs

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/09303448.pdf?arnumber=9303448

Cited by 29 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Balancing Performance, Efficiency and Robustness in Open-World Machine Learning via Evolutionary Multi-objective Model Compression;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Learnable Weighted Superposition in HDC and its Application to Multi-channel Time Series Classification;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

3. A novel Reservoir Architecture for Periodic Time Series Prediction;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

4. Enhancing Time Series Predictability via Structure‐Aware Reservoir Computing;Advanced Intelligent Systems;2024-06-02

5. FPGA Implementation for Large Scale Reservoir Computing based on Chaotic Boltzmann Machine;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19