9 citations · 17 across the 9 of their papers we have counts for
11 papers
AIR-LLM: Broadcasting AI Weights over Radio for Memory-Free Edge LLM Inference via RF Computing
Zhihui Gao, Tingjun Chen, Dirk Englund
Next-generation large language models (LLMs) are expanding from the cloud to ubiquitous edge devices. However, edge devices typically either lack the memory to store increasingly l…
Radio-Frequency Convolutional Neural Networks
Zhihui Gao, Shi-Yuan Ma, Yiran Chen +2
Running artificial intelligence (AI) models directly on edge devices such as smartphones, wearables, and drones offers low latency, pervasive scalability, and data privacy, but the…
A Machine-Verified Proof of a Quantum-Optimization Conjecture
Uri Kol, Maor Ben-Shahar, Kfir Sulimany +1
We report a machine-verified resolution of a problem open for over a decade in quantum optimization: the Farhi, Goldstone and Gutmann (FGG) conjecture that depth- Quantum Approx…
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…
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…