activity
20172023
most citedCSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

168 citations · 743 across the 44 of their papers we have counts for

collaborators
Showing 2019Show all

7 papers · 1 filter

cs.LG2019★ 5 cited

NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving

Cong Hao, Yao Chen, Xinheng Liu +9

The rapidly growing demands for powerful AI algorithms in many application domains have motivated massive investment in both high-quality deep neural network (DNN) models and high-…

cs.CV2019★ 83 cited

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…

cs.AR2019

Analysis and Optimization of I/O Cache Coherency Strategies for SoC-FPGA Device

Seung Won Min, Sitao Huang, Mohamed El-Hadedy +3

Unlike traditional PCIe-based FPGA accelerators, heterogeneous SoC-FPGA devices provide tighter integrations between software running on CPUs and hardware accelerators. Modern hete…

cs.CV2019★ 20 cited

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…

cs.CV2019★ 10 cited

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…

cs.CV2019★ 13 cited

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…