16 citations · 26 across the 5 of their papers we have counts for
5 papers
PowerYOLO: Mixed Precision Model for Hardware Efficient Object Detection with Event Data
Dominika Przewlocka-Rus, Tomasz Kryjak, Marek Gorgon
The performance of object detection systems in automotive solutions must be as high as possible, with minimal response time and, due to the often battery-powered operation, low ene…
Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation
Dominika Przewlocka-Rus, Tomasz Kryjak
Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity.Since for those systems…
Towards real-time and energy efficient Siamese tracking -- a hardware-software approach
Dominika Przewlocka-Rus, Tomasz Kryjak
Siamese trackers have been among the state-of-the-art solutions in each Visual Object Tracking (VOT) challenge over the past few years. However, with great accuracy comes great com…
Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks
Dominika Przewlocka-Rus, Syed Shakib Sarwar, H. Ekin Sumbul +2
Deploying Deep Neural Networks in low-power embedded devices for real time-constrained applications requires optimization of memory and computational complexity of the networks, us…
Exploration of Hardware Acceleration Methods for an XNOR Traffic Signs Classifier
Dominika Przewlocka-Rus, Marcin Kowalczyk, Tomasz Kryjak
Deep learning algorithms are a key component of many state-of-the-art vision systems, especially as Convolutional Neural Networks (CNN) outperform most solutions in the sense of ac…