5,021 research outputs found
Towards a Scalable Hardware/Software Co-Design Platform for Real-time Pedestrian Tracking Based on a ZYNQ-7000 Device
Currently, most designers face a daunting task to
research different design flows and learn the intricacies of
specific software from various manufacturers in
hardware/software co-design. An urgent need of creating a
scalable hardware/software co-design platform has become a key
strategic element for developing hardware/software integrated
systems. In this paper, we propose a new design flow for building
a scalable co-design platform on FPGA-based system-on-chip.
We employ an integrated approach to implement a histogram
oriented gradients (HOG) and a support vector machine (SVM)
classification on a programmable device for pedestrian tracking.
Not only was hardware resource analysis reported, but the
precision and success rates of pedestrian tracking on nine open
access image data sets are also analysed. Finally, our proposed
design flow can be used for any real-time image processingrelated
products on programmable ZYNQ-based embedded
systems, which benefits from a reduced design time and provide a
scalable solution for embedded image processing products
Efficiency analysis methodology of FPGAs based on lost frequencies, area and cycles
We propose a methodology to study and to quantify efficiency and the impact of overheads on runtime performance. Most work on High-Performance Computing (HPC) for FPGAs only studies runtime performance or cost, while we are interested in how far we are from peak performance and, more importantly, why. The efficiency of runtime performance is defined with respect to the ideal computational runtime in absence of inefficiencies. The analysis of the difference between actual and ideal runtime reveals the overheads and bottlenecks. A formal approach is proposed to decompose the efficiency into three components: frequency, area and cycles. After quantification of the efficiencies, a detailed analysis has to reveal the reasons for the lost frequencies, lost area and lost cycles. We propose a taxonomy of possible causes and practical methods to identify and quantify the overheads. The proposed methodology is applied on a number of use cases to illustrate the methodology. We show the interaction between the three components of efficiency and show how bottlenecks are revealed
Resource-constrained FPGA Design for Satellite Component Feature Extraction
The effective use of computer vision and machine learning for on-orbit
applications has been hampered by limited computing capabilities, and therefore
limited performance. While embedded systems utilizing ARM processors have been
shown to meet acceptable but low performance standards, the recent availability
of larger space-grade field programmable gate arrays (FPGAs) show potential to
exceed the performance of microcomputer systems. This work proposes use of
neural network-based object detection algorithm that can be deployed on a
comparably resource-constrained FPGA to automatically detect components of
non-cooperative, satellites on orbit. Hardware-in-the-loop experiments were
performed on the ORION Maneuver Kinematics Simulator at Florida Tech to compare
the performance of the new model deployed on a small, resource-constrained FPGA
to an equivalent algorithm on a microcomputer system. Results show the FPGA
implementation increases the throughput and decreases latency while maintaining
comparable accuracy. These findings suggest future missions should consider
deploying computer vision algorithms on space-grade FPGAs.Comment: 9 pages, 7 figures, 4 tables, Accepted at IEEE Aerospace Conference
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