195 citations · 835 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…