Hierarchical Network Connectivity and Partitioning for Reconfigurable Large-Scale Neuromorphic Systems

We present an efficient and scalable partitioning method for mapping large-scale neural network models with locally dense and globally sparse connectivity onto reconfigurable neuromorphic hardware. Scalability in computational efficiency, i.e., amount of time spent in actual computation, remains a h...

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Detalles Bibliográficos
Autores principales: Mysore, Nishant, Hota, Gopabandhu, Deiss, Stephen R., Pedroni, Bruno U., Cauwenberghs, Gert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842996/
https://www.ncbi.nlm.nih.gov/pubmed/35173573
http://dx.doi.org/10.3389/fnins.2021.797654

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842996/
https://www.ncbi.nlm.nih.gov/pubmed/35173573
http://dx.doi.org/10.3389/fnins.2021.797654

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