Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Deutel, Mark"'
This work-in-progress paper presents results on the feasibility of single-shot object detection on microcontrollers using YOLO. Single-shot object detectors like YOLO are widely used, however due to their complexity mainly on larger GPU-based platfor
Externí odkaz:
http://arxiv.org/abs/2408.15865
On-device training of DNNs allows models to adapt and fine-tune to newly collected data or changing domains while deployed on microcontroller units (MCUs). However, DNN training is a resource-intensive task, making the implementation and execution of
Externí odkaz:
http://arxiv.org/abs/2407.10734
Deploying Deep Neural Networks (DNNs) on microcontrollers (TinyML) is a common trend to process the increasing amount of sensor data generated at the edge, but in practice, resource and latency constraints make it difficult to find optimal DNN candid
Externí odkaz:
http://arxiv.org/abs/2305.14109
Publikováno v:
MBMV 26 (2023) 13-24
Large Deep Neural Networks (DNNs) are the backbone of today's artificial intelligence due to their ability to make accurate predictions when being trained on huge datasets. With advancing technologies, such as the Internet of Things, interpreting lar
Externí odkaz:
http://arxiv.org/abs/2205.10369
Deploying Deep Neural Networks (DNNs) on tiny devices is a common trend to process the increasing amount of sensor data being generated. Multi-objective optimization approaches can be used to compress DNNs by applying network pruning and weight quant
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6ff79c5f7f2f3ba1245f9e5b6dfefe22
Publikováno v:
ACM International Conference Proceeding Series; 8/25/2020, p1-6, 6p
