394 citations · 984 across the 9 of their papers we have counts for
10 papers
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…
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…
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…
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…
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…
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…