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Alexander N. Tait
Researcher at National Institute of Standards and Technology
Publications - 162
Citations - 5962
Alexander N. Tait is an academic researcher from National Institute of Standards and Technology. The author has contributed to research in topics: Photonics & Neuromorphic engineering. The author has an hindex of 31, co-authored 141 publications receiving 3287 citations. Previous affiliations of Alexander N. Tait include University of Rochester & Princeton University.
Papers
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Journal ArticleDOI
Neuromorphic photonic networks using silicon photonic weight banks.
Alexander N. Tait,Thomas Ferreira de Lima,Ellen Zhou,Allie X. Wu,Mitchell A. Nahmias,Bhavin J. Shastri,Paul R. Prucnal +6 more
TL;DR: First observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks are reported, and a mathematical isomorphism between the silicon photonics circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis.
Journal ArticleDOI
Photonics for artificial intelligence and neuromorphic computing
Bhavin J. Shastri,Bhavin J. Shastri,Alexander N. Tait,Alexander N. Tait,T. Ferreira de Lima,Wolfram H. P. Pernice,Harish Bhaskaran,C.D. Wright,Paul R. Prucnal +8 more
TL;DR: In this paper, the authors review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.
Journal ArticleDOI
Photonics for artificial intelligence and neuromorphic computing
Bhavin J. Shastri,Alexander N. Tait,Thomas Ferreira de Lima,Wolfram H. P. Pernice,Harish Bhaskaran,C. David Wright,Paul R. Prucnal +6 more
TL;DR: Recent advances in integrated photonic neuromorphic neuromorphic systems are reviewed, current and future challenges are discussed, and the advances in science and technology needed to meet those challenges are outlined.
Journal ArticleDOI
Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing
TL;DR: An on-chip optical architecture to support massive parallel communication among high-performance spiking laser neurons and a novel approach to photonic spike processing represents a promising opportunity in unconventional computing is suggested.
Journal ArticleDOI
A Leaky Integrate-and-Fire Laser Neuron for Ultrafast Cognitive Computing
TL;DR: An original design for a neuron-inspired photonic computational primitive for a large-scale, ultrafast cognitive computing platform that exhibits excitability and behaves analogously to a leaky integrate-and-fire (LIF) neuron is proposed.