Technical worskhop on Integrated machine-learning hardware for near-sensor computing applications

#machine #learning #near #sensor #computing #integrated #circuit

This is a technical workshop co-spondored by IEEE CAS France, GDR SoC2 and the Research Council of the Catholic University of Lille

With the growing amount of Smart Sensors, decreasing the energy consumption of the devices must be a priority to increase the battery lifetime and enable wearable and continuous monitoring. Since communication interfaces are the most energy-hungry parts of the sensor nodes, the “Near-Sensor Computing” concept aims at pre-processing the input data in order to keep only relevant information and thus limit the amount of data to transmit. Machine learning techniques are used to determine the relevance depending on the targeted application. The objective of this workshop is to detail how the embedded processing circuits can be integrated into the hardware and interfaced as close as possible to the sensor.

Seven excellent speakers are scheduled to cover many aspects of integrated processing and machine learning hardware, including a distinguished lecturer from the IEEE Circuit and Systems Society. The application fields range from biomedical signals (EEG, ECG) to audio signals (silicon cochlea, voice activity detection) to vision and general concepts of analog-to-feature conversion. The contributions will cover circuit-level, system-level and integration challenges.

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• Date: 18 Nov 2019
• Time: 10:00 AM to 05:00 PM
• All times are (UTC+01:00) Paris
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• IEMN
• Cité Scientifique Avenue Henri Poincaré
• Villeneuve d'Ascq, Nord-Pas-de-Calais
• France 59652
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• Starts 04 September 2019 11:18 AM
• Ends 13 November 2019 11:59 PM
• All times are (UTC+01:00) Paris
• No Admission Charge

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Schedule :

10h00 – 11h30: Jerald Yoo, National University of Singapore, IEEE CASS Distinguished Lecturer

On-Chip Epilepsy Detection: Where Machine Learning Meets Wearable, Patient-Specific Wearable Healthcare

11h30 – 12h30: Minhao Yang, EPFL

Towards Near-Zero-Power Audio Inference Sensing

12h30 – 14h00: Lunch Break

14h00 – 14h30: Deepu John, UC Dublin

Low Power Sensor Design for Wearable Health Monitoring

14h30 – 15h00: Benoit Larras, IEMN, Yncréa ISEN

Distributed Clique-Based Neural Networks for Data Fusion at the Edge

15h00 – 15h30: Jean Martinet, Université Côte d'Azur, I3S, CNRS, Polytech Nice Sophia

Towards a Neuro-Inspired Machine Learning for Vision

15h30 – 15h45: Coffee break

15h45 – 16h15: Sébastien Pecqueur, IEMN

Sensing Paradigms in a Neuromorphic Framework: What are the New Sensing Hardware Figure-of-Merits?

16h15 – 16h45: Antoine Back, LTCI, Télécom Paris, Institut Polytechnique de Paris

Feature Selection Algorithms for the Design of a Flexible Analog-To-Feature Converter