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Re-SENSE SIGNED

RESOURCE-EFFICIENT SENSING THROUGH DYNAMIC ATTENTION-SCALABILITY

Total Cost €

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EC-Contrib. €

0

Partnership

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 Re-SENSE project word cloud

Explore the words cloud of the Re-SENSE project. It provides you a very rough idea of what is the project "Re-SENSE" about.

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Project "Re-SENSE" data sheet

The following table provides information about the project.

Coordinator
KATHOLIEKE UNIVERSITEIT LEUVEN 

Organization address
address: OUDE MARKT 13
city: LEUVEN
postcode: 3000
website: www.kuleuven.be

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Belgium [BE]
 Total cost 1˙484˙562 €
 EC max contribution 1˙484˙562 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2016-STG
 Funding Scheme ERC-STG
 Starting year 2017
 Duration (year-month-day) from 2017-03-01   to  2022-02-28

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    KATHOLIEKE UNIVERSITEIT LEUVEN BE (LEUVEN) coordinator 1˙484˙562.00

Map

 Project objective

It is hard to stand on one leg if we close our eyes. We have trouble tasting food without smelling. And when we talk with other people, we observe their lips to understand them better. We, humans, are masters in sensor fusion as we can seamlessly combine information coming from different senses to improve our judgements. Intriguingly, in order to fuse information efficiently, we do not always devote the same level of attention or mental effort to each of the many sensory streams available to us. This dynamic attention-scalability allows us to always extract the maximum amount of relevant information under our limited human computational bandwidth.

Would it not be great if electronics had the same capabilities? While many devices are nowadays equipped with a massive amount of sensors, they typically cannot effectively fuse more than a few of them. The rigid way in which sensory data is combined results in large computational workloads, preventing effective multi-sensor fusion in resource-constrained applications such as robotics, wearables, biomedical monitoring or user interfacing.

The Re-SENSE project will bring attention-scalable sensing to resource-scarce devices, which are constrained in terms of energy, throughput, latency or memory resources. This is achieved by jointly: 1) Developing resource-aware inference and fusion algorithms, which maximize information capture in function of hardware resource usage, dynamically tuning sensory attention levels 2) Implementing dynamic, wide-range resource-scalable inference processors, allowing to exploit this attention-scalability for drastically improved efficiency The attention-scalable sensing concept will be demonstrated in 2 highly resource-constrained applications: a) latency-critical cell sorting and b) energy-critical epilepsy monitoring. This combination of processor design, reconfigurable hardware and embedded machine learning fits perfectly to the PI’s expertise gained at Intel Labs, UC Berkeley and KULeuven.

 Publications

year authors and title journal last update
List of publications.
2019 L Galindez Olascoaga, W. Meert, M. Verhelst, G. Van den Broeck
Towards Hardware-Aware Tractable Learning of Probabilistic Models (workshop version)
published pages: , ISSN: , DOI:
3rd Tractable Probabilistic Modeling Workshop colocated with the 36th International Conference on Machine Learning (TPM-ICML 2019) 2019-11-08
2019 Nimish Shah, Laura I. Galindez Olascoaga, Wannes Meert and Marian Verhelst
PRU: Probabilistic Reasoning processing Unit for resource-efficient AI
published pages: , ISSN: , DOI:
HotChips 2019-10-29
2017 Marian Verhelst, Bert Moons
Embedded Deep Neural Network Processing: Algorithmic and Processor Techniques Bring Deep Learning to IoT and Edge Devices
published pages: 55-65, ISSN: 1943-0582, DOI: 10.1109/mssc.2017.2745818
IEEE Solid-State Circuits Magazine 9/4 2019-10-29
2019 Laura I. Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst, Guy Van den Broeck
Towards Hardware-Aware Tractable Learning of Probabilistic Models
published pages: , ISSN: , DOI:
Accepted for Publication at Proceedings of the Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019). 2019-10-29
2018 Thomas Bos, Komail Badami, Wim Dehaene, Marian Verhelst
Architecture optimization for energy-efficient resolution-scalable 8–12-bit SAR ADCs
published pages: 437-448, ISSN: 0925-1030, DOI: 10.1007/s10470-018-1235-0
Analog Integrated Circuits and Signal Processing 97/3 2019-10-29
2018 Laura Galindez, Komail Badami, Jonas Vlasselaer, Wannes Meert, Marian Verhelst
Dynamic Sensor-Frontend Tuning for Resource Efficient Embedded Classification
published pages: 1-1, ISSN: 2156-3357, DOI: 10.1109/JETCAS.2018.2850451
IEEE Journal on Emerging and Selected Topics in Circuits and Systems 2019-06-13
2018 Thomas Bos, Komail Badami, Wim Dehaene, Marian Verhelst
Architecture optimization for energy-efficient resolution-scalable 8–12-bit SAR ADCs
published pages: , ISSN: 0925-1030, DOI: 10.1007/s10470-018-1235-0
Analog Integrated Circuits and Signal Processing 2019-06-13
2018 Koen Goetschalckx, Bert Moons, Steven Lauwereins, Martin Andraud, Marian Verhelst
Optimized Hierarchical Cascaded Processing
published pages: 1-1, ISSN: 2156-3357, DOI: 10.1109/JETCAS.2018.2839347
IEEE Journal on Emerging and Selected Topics in Circuits and Systems 2019-06-13

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