Technical Workshop on Integrated machine-learning hardware for near-sensor computing applications
Technical workshop sponsored by IEEE-CAS and GDR SOC2
November 18th, 2019
IEMN, Villeneuve d’Ascq, France
Organizers: Kévin Hérissé, Benoit Larras, Antoine Frappé (Univ. Lille, Yncréa Hauts-de-France, IEMN)
Registration is mandatory!
More details and registration at
With the growing amount of Smart Sensors, decreasing the energy consumption of the devices must be a priority to increase the batteries 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.
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, Univ. Lille, Yncréa Hauts-De-France, IEMN
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