394 citations · 2.7k across the 50 of their papers we have counts for
51 papers
Homodyne Photonic Tensor Processor exceeds 1,000-TOPS
Lian Zhou, Kaiwen Xue, Yun-Jhu Lee +10
High-performance computing underpins modern artificial intelligence (AI), enabling foundation models, real-time inference and perception in autonomous systems, and data-intensive s…
Quantization-aware Photonic Homodyne computing for Accelerated Artificial Intelligence and Scientific Simulation
Lian Zhou, Kaiwen Xue, Amirhossein Fallah +14
Modern problems in high-performance computing, ranging from training and inferencing deep learning models in computer vision and language models to simulating complex physical syst…
Demonstration and Non-volatile Trimming of a Highly-Parallel, High-Capacity Silicon Microdisk Transmitter
Chao Luan, Alex Sludds, Chao Li +3
Optical interconnects are the most promising solution to address the data-movement bottleneck in data centers. Silicon microdisks, benefiting from their compact footprint, low ener…
Single-Shot Matrix-Matrix Multiplication Optical Tensor Processor for Deep Learning
Chao Luan, Ronald Davis, Zaijun Chen +2
The ever-increasing data demand craves advancements in high-speed and energy-efficient computing hardware. Analog optical neural network (ONN) processors have emerged as a promisin…
Quantum-secure multiparty deep learning
Kfir Sulimany, Sri Krishna Vadlamani, Ryan Hamerly +2
Secure multiparty computation enables the joint evaluation of multivariate functions across distributed users while ensuring the privacy of their local inputs. This field has becom…
Towards the Information-Theoretic Limit of Programmable Photonics
Ryan Hamerly, Jasvith Raj Basani, Alexander Sludds +2
The scalability of many programmable photonic circuits is limited by the tuning range needed for the constituent phase shifters. To address this problem, we introduce the conc…
Hybrid AM/FM Mode-Locking of Singly-Resonant OPOs
Ryan Hamerly, Evan Laksono, Marc Jankowski +4
We investigate a new mode-locking regime in the singly-resonant OPO employing simultaneous amplitude- and frequency-modulation of the intracavity field. This OPO exhibits determini…
Ultrafast second-order nonlinear photonics -- from classical physics to non-Gaussian quantum dynamics
Marc Jankowski, Ryotatsu Yanagimoto, Edwin Ng +4
Photonic integrated circuits with second-order () nonlinearities are rapidly scaling to remarkably low powers. At this time, state-of-the-art devices achieve saturated non…
Hypermultiplexed Integrated-Photonics-based Tensor Optical Processor
Shaoyuan Ou, Kaiwen Xue, Lian Zhou +11
The escalating data volume and complexity resulting from the rapid expansion of artificial intelligence (AI), internet of things (IoT) and 5G/6G mobile networks is creating an urge…
Mesoscopic ultrafast nonlinear optics -- The emergence of multimode quantum non-Gaussian physics
Ryotatsu Yanagimoto, Edwin Ng, Marc Jankowski +8
Over the last few decades, nonlinear optics has become significantly more nonlinear, traversing nearly a billionfold improvement in energy efficiency, with ultrafast nonlinear nano…
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…
Asymptotically Fault-Tolerant Programmable Photonics
Ryan Hamerly, Saumil Bandyopadhyay, Dirk Englund
Component errors limit the scaling of programmable coherent photonic circuits. These errors arise because the standard tunable photonic coupler -- the Mach-Zehnder interferometer (…
Stability of Self-Configuring Large Multiport Interferometers
Ryan Hamerly, Saumil Bandyopadhyay, Dirk Englund
Realistic multiport interferometers (beamsplitter meshes) are sensitive to component imperfections, and this sensitivity increases with size. Self-configuration techniques can be e…
Accurate Self-Configuration of Rectangular Multiport Interferometers
Ryan Hamerly, Saumil Bandyopadhyay, Dirk Englund
Multiport interferometers based on integrated beamsplitter meshes are widely used in photonic technologies. While the rectangular mesh is favored for its compactness and uniformity…