20 citations · 70 across the 9 of their papers we have counts for
6 papers · 1 filter
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
Xiaofan Zhang, Haoming Lu, Cong Hao +9
Object detection and tracking are challenging tasks for resource-constrained embedded systems. While these tasks are among the most compute-intensive tasks from the artificial inte…
SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection
Xiaofan Zhang, Cong Hao, Haoming Lu +9
Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding re…
A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices
Xiaofan Zhang, Cong Hao, Yuhong Li +4
Developing deep learning models for resource-constrained Internet-of-Things (IoT) devices is challenging, as it is difficult to achieve both good quality of results (QoR), such as…
FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge
Cong Hao, Xiaofan Zhang, Yuhong Li +5
While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA…
Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs
Chuanhao Zhuge, Xinheng Liu, Xiaofan Zhang +3
Deep Convolutional Neural Networks have become a Swiss knife in solving critical artificial intelligence tasks. However, deploying deep CNN models for latency-critical tasks remain…
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
Yuhong Li, Xiaofan Zhang, Deming Chen
We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurat…