Neuromorphic Electronic Systems for Reservoir Computing
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Neuromorphic Electronic Systems for Reservoir Computing. / Hadäghi, Fatemeh.
Reservoir Computing. Springer Science+Business Media Singapore Private Limited, 2019. S. 221-237.Publikationen: SCORING: Beitrag in Buch/Sammelwerk › SCORING: Beitrag in Sammelwerk › Forschung › Begutachtung
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TY - CHAP
T1 - Neuromorphic Electronic Systems for Reservoir Computing
AU - Hadäghi, Fatemeh
PY - 2019
Y1 - 2019
N2 - This chapter provides a comprehensive survey of the researches and motivations for hardware implementation of reservoir computing (RC) on neuromorphic electronic systems. Due to its computational efficiency and the fact that training amounts to a simple linear regression, both spiking and non-spiking implementations of reservoir computing on neuromorphic hardware have been developed. Here, a review of these experimental studies is provided to illustrate the progress in this area and to address the technical challenges which arise from this specific hardware implementation. Moreover, to deal with challenges of computation on such unconventional substrates, several lines of potential solutions are presented based on advances in other computational approaches in machine learning.
AB - This chapter provides a comprehensive survey of the researches and motivations for hardware implementation of reservoir computing (RC) on neuromorphic electronic systems. Due to its computational efficiency and the fact that training amounts to a simple linear regression, both spiking and non-spiking implementations of reservoir computing on neuromorphic hardware have been developed. Here, a review of these experimental studies is provided to illustrate the progress in this area and to address the technical challenges which arise from this specific hardware implementation. Moreover, to deal with challenges of computation on such unconventional substrates, several lines of potential solutions are presented based on advances in other computational approaches in machine learning.
UR - https://arxiv.org/pdf/1908.09572.pdf
U2 - https://doi.org/10.1007/978-981-13-1687-6_10
DO - https://doi.org/10.1007/978-981-13-1687-6_10
M3 - SCORING: Contribution to collected editions/anthologies
SN - 978-981-13-1686-9
SP - 221
EP - 237
BT - Reservoir Computing
PB - Springer Science+Business Media Singapore Private Limited
ER -
