29 citations · 74 across the 8 of their papers we have counts for
10 papers
Enabling Deep Learning on Edge Devices
Zhongnan Qu
Deep neural networks (DNNs) have succeeded in many different perception tasks, e.g., computer vision, natural language processing, reinforcement learning, etc. The high-performed D…
DRESS: Dynamic REal-time Sparse Subnets
Zhongnan Qu, Syed Shakib Sarwar, Xin Dong +3
The limited and dynamically varied resources on edge devices motivate us to deploy an optimized deep neural network that can adapt its sub-networks to fit in different resource con…
p-Meta: Towards On-device Deep Model Adaptation
Zhongnan Qu, Zimu Zhou, Yongxin Tong +1
Data collected by IoT devices are often private and have a large diversity across users. Therefore, learning requires pre-training a model with available representative data sample…
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems
Xin Dong, Barbara De Salvo, Meng Li +4
We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to…
Measuring what Really Matters: Optimizing Neural Networks for TinyML
Lennart Heim, Andreas Biri, Zhongnan Qu +1
With the surge of inexpensive computational and memory resources, neural networks (NNs) have experienced an unprecedented growth in architectural and computational complexity. Intr…
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
Fan Lu, Guang Chen, Yinlong Liu +2
Keypoint detector and descriptor are two main components of point cloud registration. Previous learning-based keypoint detectors rely on saliency estimation for each point or farth…
Deep Partial Updating: Towards Communication Efficient Updating for On-device Inference
Zhongnan Qu, Cong Liu, Lothar Thiele
Emerging edge intelligence applications require the server to retrain and update deep neural networks deployed on remote edge nodes to leverage newly collected data samples. Unfort…
Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream Dataset
Bin Li, Hu Cao, Zhongnan Qu +3
Robotic grasping plays an important role in the field of robotics. The current state-of-the-art robotic grasping detection systems are usually built on the conventional vision, suc…
Adaptive Loss-aware Quantization for Multi-bit Networks
Zhongnan Qu, Zimu Zhou, Yun Cheng +1
We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate…
Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture
Guang Chen, Shu Liu, Kejia Ren +4
The rapid growth of IoT era is shaping the future of mobile services. Advanced communication technology enables a heterogeneous connectivity where mobile devices broadcast informat…