5 citations · 6 across the 5 of their papers we have counts for
5 papers
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning
Shanglin Zhou, Mikhail A. Bragin, Lynn Pepin +3
Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline significantly increases the…
Physics-aware Roughness Optimization for Diffractive Optical Neural Networks
Shanglin Zhou, Yingjie Li, Minhan Lou +4
As a representative next-generation device/circuit technology beyond CMOS, diffractive optical neural networks (DONNs) have shown promising advantages over conventional deep neural…
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
Hongwu Peng, Shanglin Zhou, Yukui Luo +11
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns. To address these issues, secure Two-party computation (2PC) has been proposed as…
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles
Songyang Han, Shanglin Zhou, Lynn Pepin +3
The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and ca…
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System
Sahidul Islam, Shanglin Zhou, Ran Ran +4
IoT devices are increasingly being implemented with neural network models to enable smart applications. Energy harvesting (EH) technology that harvests energy from ambient environm…