Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Lujàn, Mikel"'
To reduce the source of potential exploits, binary debloating or specialization tools are used to remove unnecessary code from binaries. This paper presents a new binary debloating and specialization tool, LeanBin, that harnesses lifting and recompil
Externí odkaz:
http://arxiv.org/abs/2406.16162
Existing SLAM (Simultaneous Localization and Mapping) algorithms have achieved remarkable localization accuracy in dynamic environments by using deep learning techniques to identify dynamic objects. However, they usually require GPUs to operate in re
Externí odkaz:
http://arxiv.org/abs/2405.07392
Autor:
Iordanou, Konstantinos, Atkinson, Timothy, Ozer, Emre, Kufel, Jedrzej, Biggs, John, Brown, Gavin, Lujan, Mikel
A typical machine learning (ML) development cycle for edge computing is to maximise the performance during model training and then minimise the memory/area footprint of the trained model for deployment on edge devices targeting CPUs, GPUs, microcontr
Externí odkaz:
http://arxiv.org/abs/2303.00031
Autor:
Agiakatsikas, Dimitris, Foutris, Nikos, Sari, Aitzan, Vlagkoulis, Vasileios, Souvatzoglou, Ioanna, Psarakis, Mihalis, Ye, Ruiqi, Goodacre, John, Lujan, Mikel, Kastrioto, Maria, Cazzaniga, Carlo, Frost, Chris
The AMD UltraScale+ XCZU9EG device is a Multi-Processor System-on-Chip (MPSoC) with embedded Programmable Logic (PL) that excels in many Edge (e.g., automotive or avionics) and Cloud (e.g., data centres) terrestrial applications. However, it incorpor
Externí odkaz:
http://arxiv.org/abs/2303.08098
Publikováno v:
Journal of Machine Learning Research, 24(359), 2023
We present a theory of ensemble diversity, explaining the nature of diversity for a wide range of supervised learning scenarios. This challenge has been referred to as the holy grail of ensemble learning, an open research issue for over 30 years. Our
Externí odkaz:
http://arxiv.org/abs/2301.03962
Autor:
Song, Xidan, Manino, Edoardo, Sena, Luiz, Alves, Erickson, Filho, Eddie de Lima, Bessa, Iury, Lujan, Mikel, Cordeiro, Lucas
QNNVerifier is the first open-source tool for verifying implementations of neural networks that takes into account the finite word-length (i.e. quantization) of their operands. The novel support for quantization is achieved by employing state-of-the-
Externí odkaz:
http://arxiv.org/abs/2111.13110
Progress in the last decade has brought about significant improvements in the accuracy and speed of SLAM systems, broadening their mapping capabilities. Despite these advancements, long-term operation remains a major challenge, primarily due to the w
Externí odkaz:
http://arxiv.org/abs/2109.13160
Energy use is a key concern when deploying deep learning models on mobile and embedded platforms. Current studies develop energy predictive models based on application-level features to provide researchers a way to estimate the energy consumption of
Externí odkaz:
http://arxiv.org/abs/2004.05137
Autor:
Zhang, Wenzhe, Lu, Kai, Wang, Ruibo, Chi, Wanqing, Shao, Mingtian, Wu, Huijun, Luján, Mikel, Wang, Xiaoping
Modern operating systems all support multi-users that users could share a computer simultaneously and not affect each other. However, there are some limitations. For example, privacy problem exists that users are visible to each other in terms of run
Externí odkaz:
http://arxiv.org/abs/1912.03887
Predicting the execution time of queries is an important problem with applications in scheduling, service level agreements and error detection. During query planning, a cost is associated with the chosen execution plan and used to rank competing plan
Externí odkaz:
http://arxiv.org/abs/1905.00774