29 citations · 74 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…