157 citations · 339 across the 6 of their papers we have counts for
6 papers
When Deep Learning Meets Digital Image Correlation
S. Boukhtache, K. Abdelouahab, F. Berry +3
Convolutional Neural Networks (CNNs) constitute a class of Deep Learning models which have been used in the recent past to resolve many problems in computer vision, in particular o…
The Challenge of Multi-Operand Adders in CNNs on FPGAs: How not to solve it!
Kamel Abdelouahab, François Berry, Maxime Pelcat
Convolutional Neural Networks (CNNs) are computationally intensive algorithms that currently require dedicated hardware to be executed. In the case of FPGA-Based accelerators, we p…
Accelerating CNN inference on FPGAs: A Survey
Kamel Abdelouahab, Maxime Pelcat, Jocelyn Serot +1
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of problems, ranging from speech recognition to image classification and segmentation. Th…
Tactics to Directly Map CNN graphs on Embedded FPGAs
Kamel Abdelouahab, Maxime Pelcat, Jocelyn Sérot +3
Deep Convolutional Neural Networks (CNNs) are the state-of-the-art in image classification. Since CNN feed forward propagation involves highly regular parallel computation, it bene…
Hardware Automated Dataflow Deployment of CNNs
Kamel Abdelouahab, Maxime Pelcat, Jocelyn Serot +3
Deep Convolutional Neural Networks (CNNs) are the state of the art systems for image classification and scene understating. However, such techniques are computationally intensive a…
A Holistic Approach for Optimizing DSP Block Utilization of a CNN implementation on FPGA
Kamel Abdelouahab, Cedric Bourrasset, Maxime Pelcat +3
Deep Neural Networks are becoming the de-facto standard models for image understanding, and more generally for computer vision tasks. As they involve highly parallelizable computat…