2 citations · 5 across the 8 of their papers we have counts for
8 papers
18-dB on-chip vacuum squeezing in an adaptively poled lithium niobate waveguide
Tushar Sanjay Karnik, Xinyi Ren, Chun-Ho Lee +16
Quantum squeezed states of light can enhance measurement sensitivity beyond classical limits and enable quantum information processing, but scalable low-loss sources remain challen…
Machine Intelligence on Wireless Edge Networks
Sri Krishna Vadlamani, Kfir Sulimany, Zhihui Gao +2
Machine intelligence on edge devices enables low-latency processing and improved privacy, but is often limited by the energy and delay of moving and converting data. Current system…
Disaggregated Deep Learning via In-Physics Computing at Radio Frequency
Zhihui Gao, Sri Krishna Vadlamani, Kfir Sulimany +2
Modern edge devices, such as cameras, drones, and Internet-of-Things nodes, rely on deep learning to enable a wide range of intelligent applications, including object recognition,…
Microring Perceptron Sensing for Low-Power Radio-Frequency Detection with Quantum-Compatible Photonic Preprocessing
Bo-Han Wu, Shi-Yuan Ma, Mahmoud Jalali Mehrabad +4
Radio-frequency (RF) sensing underpins applications ranging from radar and wireless communication to biomedical and quantum measurement, where detection sensitivity at low signal-t…
QAMNet: Fast and Efficient Optical QAM Neural Networks
Marc Gong Bacvanski, Sri Krishna Vadlamani, Kfir Sulimany +1
The energy consumption of neural network inference has become a topic of paramount importance with the growing success and adoption of deep neural networks. Analog optical neural n…
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…
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…