Cargando…
Probabilistic Spike Propagation for Efficient Hardware Implementation of Spiking Neural Networks
Spiking neural networks (SNNs) have gained considerable attention in recent years due to their ability to model temporal event streams, be trained using unsupervised learning rules, and be realized on low-power event-driven hardware. Notwithstanding the intrinsic desirable attributes of SNNs, there...
Autores principales: | Nallathambi, Abinand, Sen, Sanchari, Raghunathan, Anand, Chandrachoodan, Nitin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321434/ https://www.ncbi.nlm.nih.gov/pubmed/34335168 http://dx.doi.org/10.3389/fnins.2021.694402 |
Ejemplares similares
-
Comparison of Artificial and Spiking Neural Networks on Digital Hardware
por: Davidson, Simon, et al.
Publicado: (2021) -
Low Cost Interconnected Architecture for the Hardware Spiking Neural Networks
por: Luo, Yuling, et al.
Publicado: (2018) -
Benchmarking Highly Parallel Hardware for Spiking Neural Networks in Robotics
por: Steffen, Lea, et al.
Publicado: (2021) -
Boosting Throughput and Efficiency of Hardware Spiking Neural Accelerators Using Time Compression Supporting Multiple Spike Codes
por: Xu, Changqing, et al.
Publicado: (2020) -
Back-Propagation Learning in Deep Spike-By-Spike Networks
por: Rotermund, David, et al.
Publicado: (2019)