8 citations · 33 across the 15 of their papers we have counts for
7 papers · 1 filter
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…
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…
Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices
Sahidul Islam, Jieren Deng, Shanglin Zhou +3
Energy harvesting (EH) IoT devices that operate intermittently without batteries, coupled with advances in deep neural networks (DNNs), have opened up new opportunities for enablin…
Binary Complex Neural Network Acceleration on FPGA
Hongwu Peng, Shanglin Zhou, Scott Weitze +9
Being able to learn from complex data with phase information is imperative for many signal processing applications. Today' s real-valued deep neural networks (DNNs) have shown effi…
Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation
Deniz Gurevin, Shanglin Zhou, Lynn Pepin +4
Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline, i.e., training, pruning an…
Against Membership Inference Attack: Pruning is All You Need
Yijue Wang, Chenghong Wang, Zigeng Wang +5
The large model size, high computational operations, and vulnerability against membership inference attack (MIA) have impeded deep learning or deep neural networks (DNNs) popularit…
A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods
Tianyun Zhang, Xiaolong Ma, Zheng Zhan +7
To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categ…