Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Julian Büchel"'
Autor:
Manuel Le Gallo, Corey Lammie, Julian Büchel, Fabio Carta, Omobayode Fagbohungbe, Charles Mackin, Hsinyu Tsai, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui, Malte J. Rasch
Publikováno v:
APL Machine Learning, Vol 1, Iss 4, Pp 041102-041102-36 (2023)
Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics and the non-ideal peripheral circuitr
Externí odkaz:
https://doaj.org/article/fa2ca9179db74df2aabd9c81cc0a6597
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Abstract Mixed-signal analog/digital circuits emulate spiking neurons and synapses with extremely high energy efficiency, an approach known as “neuromorphic engineering”. However, analog circuits are sensitive to process-induced variation among t
Externí odkaz:
https://doaj.org/article/14e9f78b35774441b084b0d57ac11b34
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications. Convolutional spiking neural networks model such event-based data and develop their full energy-saving potentia
Externí odkaz:
https://doaj.org/article/9d57208df4ca46518753066eece02e6d
Publikováno v:
Scientific Reports
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Scientific Reports, 11 (1)
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Scientific Reports, 11 (1)
Mixed-signal analog/digital circuits emulate spiking neurons and synapses with extremely high energy efficiency, an approach known as "neuromorphic engineering". However, analog circuits are sensitive to process-induced variation among transistors in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::212e5af7b2a8fe40d759d2a0e90da885
https://doi.org/10.1038/s41598-021-02779-x
https://doi.org/10.1038/s41598-021-02779-x
Publikováno v:
2021 IEEE International Symposium on Circuits and Systems (ISCAS)
ISCAS
ISCAS
Efficient Balanced Networks (EBNs) are networks of spiking neurons in which excitatory and inhibitory synaptic currents are balanced on a short timescale, leading to desirable coding properties such as high encoding precision, low firing rates, and d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fac323ad15795ad1b1624884693bbfa4
