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Found 9 papers, 0 papers with code
Joint-YODNet: A Light-weight Object Detector for UAVs to Achieve Above 100fps
Small object detection via UAV (Unmanned Aerial Vehicle) images captured from drones and radar is a complex task with several formidable challenges.
AaP-ReID: Improved Attention-Aware Person Re-identification
Person re-identification (ReID) is a well-known problem in the field of computer vision.
YOLORe-IDNet: An Efficient Multi-Camera System for Person-Tracking
Its ability to track targets without prior knowledge or historical data is a significant improvement over existing systems, making it well-suited for public safety and surveillance applications.
Hard-ODT: Hardware-Friendly Online Decision Tree Learning Algorithm and System
Following this, we present Hard-ODT, a high-performance, hardware-efficient and scalable online decision tree learning system on a field-programmable gate array (FPGA) with system-level optimization techniques.
Decision Tree Based Hardware Power Monitoring for Run Time Dynamic Power Management in FPGA
A flexible architecture of the hardware power monitoring is proposed, which can be instrumented in any RTL design for runtime power estimation, dispensing with the need for extra power measurement devices.
An Ensemble Learning Approach for In-situ Monitoring of FPGA Dynamic Power
As field-programmable gate arrays become prevalent in critical application domains, their power consumption is of high concern.
Towards Efficient and Scalable Acceleration of Online Decision Tree Learning on FPGA
We further present a high-performance, hardware-efficient and scalable online decision tree learning system on a field-programmable gate array (FPGA) with system-level optimization techniques.
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications
To reduce the design effort and achieve the right balance, we propose FP-Stereo for building high-performance stereo matching pipelines on FPGAs automatically.
Machine Learning Based Routing Congestion Prediction in FPGA High-Level Synthesis
Early and accurate congestion estimation is of great benefit to guide the optimization in HLS and improve the efficiency of implementation.
