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Found 9 papers, 1 papers with code
SMOF: Streaming Modern CNNs on FPGAs with Smart Off-Chip Eviction
Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in numerous vision tasks.
From Detection to Action Recognition: An Edge-Based Pipeline for Robot Human Perception
These robots heavily rely on Human Action Recognition (HAR) to interpret human actions and intentions.
fpgaHART: A toolflow for throughput-oriented acceleration of 3D CNNs for HAR onto FPGAs
A variety of 3D CNN models were evaluated using the proposed toolflow on multiple FPGA devices, demonstrating its potential to deliver competitive performance compared to earlier hand-tuned and model-specific designs.
FMM-X3D: FPGA-based modeling and mapping of X3D for Human Action Recognition
3D Convolutional Neural Networks are gaining increasing attention from researchers and practitioners and have found applications in many domains, such as surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval.
HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA Devices
For Human Action Recognition tasks (HAR), 3D Convolutional Neural Networks have proven to be highly effective, achieving state-of-the-art results.
Open Challenges in Synthetic Speech Detection
In this paper the current status and open challenges of synthetic speech detection are addressed.
A Deep Learning Framework for Simulation and Defect Prediction Applied in Microelectronics
In particular, we propose an architecture based on 3D Convolutional Neural Networks (3DCNN) in order to model the geometric variations in manufacturing parameters and predict upcoming events related to sub-optimal performance.
Fault Diagnosis in Microelectronics Attachment via Deep Learning Analysis of 3D Laser Scans
A common source of defects in manufacturing miniature Printed Circuits Boards (PCB) is the attachment of silicon die or other wire bondable components on a Liquid Crystal Polymer (LCP) substrate.
Image-based Natural Language Understanding Using 2D Convolutional Neural Networks
We propose a new approach to natural language understanding in which we consider the input text as an image and apply 2D Convolutional Neural Networks to learn the local and global semantics of the sentences from the variations ofthe visual patterns of words.
