394 citations · 2.7k across the 50 of their papers we have counts for
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Transferable Learning on Analog Hardware
Sri Krishna Vadlamani, Dirk Englund, Ryan Hamerly
While analog neural network (NN) accelerators promise massive energy and time savings, an important challenge is to make them robust to static fabrication error. Present-day traini…
A Self-Similar Sine-Cosine Fractal Architecture for Multiport Interferometers
Jasvith Raj Basani, Sri Krishna Vadlamani, Saumil Bandyopadhyay +2
Multiport interferometers based on integrated beamsplitter meshes have recently captured interest as a platform for many emerging technologies. In this paper, we present a novel ar…
Quantum nondemolition measurements with optical parametric amplifiers for ultrafast universal quantum information processing
Ryotatsu Yanagimoto, Rajveer Nehra, Ryan Hamerly +3
Realization of a room-temperature ultra-fast photon-number-resolving (PNR) quantum nondemolition (QND) measurement would have significant implications for photonic quantum informat…
Single chip photonic deep neural network with accelerated training
Saumil Bandyopadhyay, Alexander Sludds, Stefan Krastanov +6
As deep neural networks (DNNs) revolutionize machine learning, energy consumption and throughput are emerging as fundamental limitations of CMOS electronics. This has motivated a s…
Degenerate optical parametric amplification in CMOS silicon
David Heydari, Mircea Catuneanu, Edwin Ng +7
Silicon is a common material for photonics due to its favorable optical properties in the telecom and mid-wave IR bands, as well as compatibility with a wide range of complementary…
Deep Learning with Coherent VCSEL Neural Networks
Zaijun Chen, Alexander Sludds, Ronald Davis +8
Deep neural networks (DNNs) are reshaping the field of information processing. With their exponential growth challenging existing electronic hardware, optical neural networks (ONNs…
Netcast: Low-Power Edge Computing with WDM-defined Optical Neural Networks
Ryan Hamerly, Alexander Sludds, Saumil Bandyopadhyay +4
This paper analyzes the performance and energy efficiency of Netcast, a recently proposed optical neural-network architecture designed for edge computing. Netcast performs deep neu…
RF-Photonic Deep Learning Processor with Shannon-Limited Data Movement
Ronald Davis, Zaijun Chen, Ryan Hamerly +1
Edholm's Law predicts exponential growth in data rate and spectrum bandwidth for communications and is forecasted to remain true for the upcoming deployment of 6G. Compounding this…
Single-Shot Optical Neural Network
Liane Bernstein, Alexander Sludds, Christopher Panuski +3
As deep neural networks (DNNs) grow to solve increasingly complex problems, they are becoming limited by the latency and power consumption of existing digital processors. For impro…
Delocalized Photonic Deep Learning on the Internet's Edge
Alexander Sludds, Saumil Bandyopadhyay, Zaijun Chen +13
Advances in deep neural networks (DNNs) are transforming science and technology. However, the increasing computational demands of the most powerful DNNs limit deployment on low-pow…
Temporal trapping: a route to strong coupling and deterministic optical quantum computation
Ryotatsu Yanagimoto, Edwin Ng, Marc Jankowski +2
The realization of deterministic photon-photon gates is a central goal in optical quantum computation and engineering. A longstanding challenge is that optical nonlinearities in sc…