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pro vyhledávání: '"Liberis, Edgar"'
Autor:
Liberis, Edgar, Lane, Nicholas D.
Embedded and IoT devices, largely powered by microcontroller units (MCUs), could be made more intelligent by leveraging on-device deep learning. One of the main challenges of neural network inference on an MCU is the extremely limited amount of read-
Externí odkaz:
http://arxiv.org/abs/2211.17246
Autor:
Liberis, Edgar, Lane, Nicholas D.
Embedded and personal IoT devices are powered by microcontroller units (MCUs), whose extreme resource scarcity is a major obstacle for applications relying on on-device deep learning inference. Orders of magnitude less storage, memory and computation
Externí odkaz:
http://arxiv.org/abs/2110.08350
IoT devices are powered by microcontroller units (MCUs) which are extremely resource-scarce: a typical MCU may have an underpowered processor and around 64 KB of memory and persistent storage, which is orders of magnitude fewer computational resource
Externí odkaz:
http://arxiv.org/abs/2010.14246
Machine learning, particularly deep learning, is being increasing utilised in space applications, mirroring the groundbreaking success in many earthbound problems. Deploying a space device, e.g. a satellite, is becoming more accessible to small actor
Externí odkaz:
http://arxiv.org/abs/2001.10362
Autor:
Liberis, Edgar, Lane, Nicholas D.
Designing deep learning models for highly-constrained hardware would allow imbuing many edge devices with intelligence. Microcontrollers (MCUs) are an attractive platform for building smart devices due to their low cost, wide availability, and modest
Externí odkaz:
http://arxiv.org/abs/1910.05110
Autor:
Veličković, Petar, Karazija, Laurynas, Lane, Nicholas D., Bhattacharya, Sourav, Liberis, Edgar, Liò, Pietro, Chieh, Angela, Bellahsen, Otmane, Vegreville, Matthieu
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective. For this, we design several deep long short-term memory (LSTM) arc
Externí odkaz:
http://arxiv.org/abs/1709.08073
Akademický článek
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Publikováno v:
Proceedings of the 2nd European Workshop on Machine Learning and Systems
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=sygma_______::3963597dfdf3e6d91c65bc7b4196808b