1 citations · 2 across the 2 of their papers we have counts for
5 papers
A Parameterizable Convolution Accelerator for Embedded Deep Learning Applications
Panagiotis Mousouliotis, Georgios Keramidas
Convolutional neural network (CNN) accelerators implemented on Field-Programmable Gate Arrays (FPGAs) are typically designed with a primary focus on maximizing performance, often m…
Software-Defined FPGA Accelerator Design for Mobile Deep Learning Applications
Panagiotis G. Mousouliotis, Loukas P. Petrou
Recently, the field of deep learning has received great attention by the scientific community and it is used to provide improved solutions to many computer vision problems. Convolu…
A Framework of Transfer Learning in Object Detection for Embedded Systems
Ioannis Athanasiadis, Panagiotis Mousouliotis, Loukas Petrou
Transfer learning is one of the subjects undergoing intense study in the area of machine learning. In object recognition and object detection there are known experiments for the tr…
SqueezeJet: High-level Synthesis Accelerator Design for Deep Convolutional Neural Networks
Panagiotis G. Mousouliotis, Loukas P. Petrou
Deep convolutional neural networks have dominated the pattern recognition scene by providing much more accurate solutions in computer vision problems such as object recognition and…
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment
Panagiotis G. Mousouliotis, Konstantinos L. Panayiotou, Emmanouil G. Tsardoulias +2
FPGAs are commonly used to accelerate domain-specific algorithmic implementations, as they can achieve impressive performance boosts, are reprogrammable and exhibit minimal power c…