85 citations · 153 across the 10 of their papers we have counts for
10 papers
On Hardware-Aware Design and Optimization of Edge Intelligence
Shuo Huai, Hao Kong, Xiangzhong Luo +5
Edge intelligence systems, the intersection of edge computing and artificial intelligence (AI), are pushing the frontier of AI applications. However, the complexity of deep learnin…
Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems
Shuo Huai, Di Liu, Hao Kong +5
Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while protecting data privacy. Howe…
EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI
Hao Kong, Di Liu, Shuo Huai +5
Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of CNNs hinders the deployment…
Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization
Shuo Huai, Di Liu, Hao Kong +4
Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding the number of neurons during th…
Smart Scissor: Coupling Spatial Redundancy Reduction and CNN Compression for Embedded Hardware
Hao Kong, Di Liu, Shuo Huai +5
Scaling down the resolution of input images can greatly reduce the computational overhead of convolutional neural networks (CNNs), which is promising for edge AI. However, as an im…
EdgeSphere: A Three-Tier Architecture for Cognitive Edge Computing
Christian Makaya, Keith Grueneberg, Bongjun Ko +3
Computing at the edge is increasingly important as Internet of Things (IoT) devices at the edge generate massive amounts of data and pose challenges in transporting all that data t…
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation
Zhuosheng Zhang, Jiarui Li, Shucheng Yu +1
For model privacy, local model parameters in federated learning shall be obfuscated before sent to the remote aggregator. This technique is referred to as \emph{secure aggregation}…
Cost-effective Machine Learning Inference Offload for Edge Computing
Christian Makaya, Amalendu Iyer, Jonathan Salfity +2
Computing at the edge is increasingly important since a massive amount of data is generated. This poses challenges in transporting all that data to the remote data centers and clou…
A Comprehensive Study on Load Balancers for VNF chains Horizontal Scaling
Jiefei Ma, Windhya Rankothge, Christian Makaya +3
We present an architectural design and a reference implementation for horizontal scaling of virtual network function chains. Our solution does not require any changes to network fu…
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis +4
Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learnin…