Real-Time Monitoring Tool for SNN Hardware Architecture-Reference-Cited by-同舟云学术

Real-Time Monitoring Tool for SNN Hardware Architecture

Published:2023 Issue: Volume: Page:291-302
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Zapata Mireya^ORCID,Vargas Vanessa^ORCID,Cagua Ariel,Alvarez Daniela,Vallejo Bernardo^ORCID,Madrenas Jordi^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-31183-3_24

Reference11 articles.

1. Frenkel, C., Bol, D., Indiveri, G.: Bottom-up and top-down neural processing systems design: neuromorphic intelligence as the convergence of natural and artificial intelligence (2021). arXiv preprint arXiv:2106.01288

2. Camuñas-Mesa, L.A., Linares-Barranco, B., Serrano-Gotarredona, T.: Neuromorphic spiking neural networks and their memristor-CMOS hardware implementations. Materials 12(17), 2745 (2019). https://doi.org/10.3390/ma12172745

3. Guo, W., Fouda, M.E., Eltawil, A.M., Salama, K.N.: Neural coding in spiking neural networks: a comparative study for robust neuromorphic systems. Front. Neurosci. 15(March), 1–21 (2021). https://doi.org/10.3389/fnins.2021.638474

4. Spilger, P., et al.: hxtorch: PyTorch for BrainScaleS-2: Perceptrons on analog neuromorphic hardware. Commun. Comput. Inf. Sci. 1325, 189–200 (2020). https://doi.org/10.1007/978-3-030-66770-214

5. Benjamin, B.V., et al.: Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE 102(5), 699–716 (2014). https://doi.org/10.1109/JPROC.2014.2313565

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