Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Yun-Jhu Lee"'
Autor:
Luis El Srouji, Mahmoud Abdelghany, Hari Rakul Ambethkar, Yun-Jhu Lee, Mehmet Berkay On, S. J. Ben Yoo
Publikováno v:
Frontiers in Neuroscience, Vol 18 (2024)
With the increasing number of applications reliant on large neural network models, the pursuit of more suitable computing architectures is becoming increasingly relevant. Progress toward co-integrated silicon photonic and CMOS circuits provides new o
Externí odkaz:
https://doaj.org/article/e7a6242289574259acb4a0c565aaea23
Autor:
Yun-Jhu Lee, 李昀築
105
In this study, a phased-array of zero-bias CMOS-based Capacitive Micro-machined Ultrasonic Transducers (CMUTs) with high sensitivity is developed. The device is implemented with the TSMC 0.35µm 2P4M CMOS-MEMS process. Based on the previous
In this study, a phased-array of zero-bias CMOS-based Capacitive Micro-machined Ultrasonic Transducers (CMUTs) with high sensitivity is developed. The device is implemented with the TSMC 0.35µm 2P4M CMOS-MEMS process. Based on the previous
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/50913308328386437668
Autor:
El Srouji, Luis, Abdelghany, Mahmoud, Ambethkar, Hari Rakul, Yun-Jhu Lee, On, Mehmet Berkay, Ben Yoo, S. J.
Publikováno v:
Frontiers in Neuroscience; 2024, p1-8, 8p
Publikováno v:
Optical Fiber Communication Conference (OFC) 2022.
We demonstrate, for the first time to our knowledge, a monolithically-integrated photonic interferometric imager circuit with on-chip detectors, CMOS trans-impedance-amplifiers, and associated photonic imager components. A proof-of-principle demonstr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0abc24c26cde52a04cb1c4ac66689d07
https://doi.org/10.1364/ofc.2022.tu2i.2
https://doi.org/10.1364/ofc.2022.tu2i.2
Spiking neural networks (SNN) provide a new computational paradigm capable of highly parallelized, real-time processing. Photonic devices are ideal for the design of high-bandwidth, parallel architectures matching the SNN computational paradigm. Co-i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c43cb588c4eb4971cee77764a7aff47
Photonic spiking neural networks (PSNNs) potentially offer exceptionally high throughput and energy efficiency compared to their electronic neuromorphic counterparts while maintaining their benefits in terms of event-driven computing capability. Whil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2ddbcf653b9a4acaf866a75f965b0bda
http://hdl.handle.net/11583/2972016
http://hdl.handle.net/11583/2972016
Publikováno v:
OFC
We designed, simulated, and taped-out a photonic spiking neural network on a monolithic silicon CMOS photonic platform. Benchmarking shows proposed PSNN outperforms other neuromorphic hardware with 21.09fJ/spike and 61.4 W average power at MNIST expe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ce522ab92aeaed170d8ffcad388c7fa9
https://doi.org/10.1364/ofc.2021.tu5h.5
https://doi.org/10.1364/ofc.2021.tu5h.5
Publikováno v:
Conference on Lasers and Electro-Optics.
We designed, simulated, prototyped, and experimentally demonstrated an optoelectronic neuron with excitatory and inhibitory inputs. LTSpice simulation and experimental results closely resemble the Izhikevich model, and inhibitory input negates excita
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c6e8a6a35fd68b66ad4f97e2a2051553
https://doi.org/10.1364/cleo_si.2020.sm1e.6
https://doi.org/10.1364/cleo_si.2020.sm1e.6
Conference
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