394 citations · 2.7k across the 50 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
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…
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…
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…
Hardware error correction for programmable photonics
Saumil Bandyopadhyay, Ryan Hamerly, Dirk Englund
Programmable photonic circuits of reconfigurable interferometers can be used to implement arbitrary operations on optical modes, facilitating a flexible platform for accelerating t…
Freely scalable and reconfigurable optical hardware for deep learning
Liane Bernstein, Alexander Sludds, Ryan Hamerly +3
As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available comp…
Large-Scale Optical Neural Networks based on Photoelectric Multiplication
Ryan Hamerly, Liane Bernstein, Alexander Sludds +2
Recent success in deep neural networks has generated strong interest in hardware accelerators to improve speed and energy consumption. This paper presents a new type of photonic ac…