168 citations · 192 across the 16 of their papers we have counts for
8 papers · 1 filter
AdROD: HyperNetwork-based Adversarially Robust Object Detection for Autonomous Driving
Yuting Wu, Dongfang Guo, Xiangzhong Luo +2
Camera-based object detectors are vulnerable to physical adversarial attacks designed to suppress detections. While adversarial training and input purification offer some protectio…
EvoLP: Self-Evolving Latency Predictor for Model Compression in Real-Time Edge Systems
Shuo Huai, Hao Kong, Shiqing Li +5
Edge devices are increasingly utilized for deploying deep learning applications on embedded systems. The real-time nature of many applications and the limited resources of edge dev…
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…
Exploring Deep-to-Shallow Transformable Neural Networks for Intelligent Embedded Systems
Xiangzhong Luo, Weichen Liu
Thanks to the evolving network depth, convolutional neural networks (CNNs) have achieved remarkable success across various embedded scenarios, paving the way for ubiquitous embedde…
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
Xiangzhong Luo, Di Liu, Hao Kong +4
Deep neural networks (DNNs) have recently achieved impressive success across a wide range of real-world vision and language processing tasks, spanning from image classification to…
You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms
Xiangzhong Luo, Di Liu, Hao Kong +3
Benefiting from the search efficiency, differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural…
HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search
Xiangzhong Luo, Di Liu, Shuo Huai +1
In this paper, we present a novel multi-objective hardware-aware neural architecture search (NAS) framework, namely HSCoNAS, to automate the design of deep neural networks (DNNs) w…
Bringing AI To Edge: From Deep Learning's Perspective
Di Liu, Hao Kong, Xiangzhong Luo +2
Edge computing and artificial intelligence (AI), especially deep learning for nowadays, are gradually intersecting to build a novel system, called edge intelligence. However, the d…