2 citations · 3 across the 3 of their papers we have counts for
7 papers
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…
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,…
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…
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…