BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Paris BEGIN:DAYLIGHT DTSTART:20200329T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20191027T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20191123T141138Z UID:0CF0455B-85FC-43F5-9C0F-0D4EBA1F4712 DTSTART;TZID=Europe/Paris:20191118T100000 DTEND;TZID=Europe/Paris:20191118T170000 DESCRIPTION:This is a technical workshop co-spondored by IEEE CAS France\, GDR SoC2 and the Research Council of the Catholic University of Lille\n\nW ith the growing amount of Smart Sensors\, decreasing the energy consumptio n of the devices must be a priority to increase the battery lifetime and e nable wearable and continuous monitoring. Since communication interfaces a re the most energy-hungry parts of the sensor nodes\, the “Near-Sensor C omputing” 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 h ow the embedded processing circuits can be integrated into the hardware an d interfaced as close as possible to the sensor.\n\nSeven excellent speake rs are scheduled to cover many aspects of integrated processing and machin e learning hardware\, including a distinguished lecturer from the IEEE Cir cuit and Systems Society. The application fields range from biomedical sig nals (EEG\, ECG) to audio signals (silicon cochlea\, voice activity detect ion) to vision and general concepts of analog-to-feature conversion. The c ontributions will cover circuit-level\, system-level and integration chall enges.\n\n[LINK TO THE DETAILS (ABSTRACTS)](https://microelecsi.iemn.fr/wp -content/uploads/sites/6/2019/10/Annonce_Journée_Machine-learning.pdf)\n \n[LINK TO THE POSTER](https://microelecsi.iemn.fr/wp-content/uploads/site s/6/2019/10/TechWS-V4.pdf)\n\nAgenda: \nSchedule :\n\n10h00 – 11h30: Jer ald Yoo\, National University of Singapore\, IEEE CASS Distinguished Lectu rer\n\nOn-Chip Epilepsy Detection: Where Machine Learning Meets Wearable\, Patient-Specific Wearable Healthcare\n\n11h30 – 12h30: Minhao Yang\, EP FL\n\nTowards Near-Zero-Power Audio Inference Sensing\n\n12h30 – 14h00: Lunch Break\n\n14h00 – 14h30: Deepu John\, UC Dublin\n\nLow Power Sensor Design for Wearable Health Monitoring\n\n14h30 – 15h00: Benoit Larras\, IEMN\, Yncréa ISEN\n\nDistributed Clique-Based Neural Networks for Data Fusion at the Edge\n\n15h00 – 15h30: Jean Martinet\, Université Côte d 'Azur\, I3S\, CNRS\, Polytech Nice Sophia\n\nTowards a Neuro-Inspired Mach ine Learning for Vision\n\n15h30 – 15h45: Coffee break\n\n15h45 – 16h1 5: Sébastien Pecqueur\, IEMN\n\nSensing Paradigms in a Neuromorphic Frame work: What are the New Sensing Hardware Figure-of-Merits?\n\n16h15 – 16h 45: Antoine Back\, LTCI\, Télécom Paris\, Institut Polytechnique de Pari s\n\nFeature Selection Algorithms for the Design of a Flexible Analog-To-F eature Converter\n\nIEMN\, Cité Scientifique Avenue Henri Poincaré\, Vil leneuve d'Ascq\, Nord-Pas-de-Calais\, France\, 59652 LOCATION:IEMN\, Cité Scientifique Avenue Henri Poincaré\, Villeneuve d'As cq\, Nord-Pas-de-Calais\, France\, 59652 ORGANIZER:antoine.frappe@yncrea.fr SEQUENCE:14 SUMMARY:Technical worskhop on Integrated machine-learning hardware for near -sensor computing applications URL;VALUE=URI:https://events.vtools.ieee.org/m/204142 X-ALT-DESC:Description: <br /><p><span style="font-size: 12pt\;"><em>This i s a technical workshop co-spondored by IEEE CAS France\, GDR SoC2 and the Research Council of the Catholic University of Lille</em></span></p>\n<p>< span style="font-size: 12pt\;">With the growing amount of Smart Sensors\, decreasing the energy consumption of the devices must be a priority to inc rease the battery lifetime and enable wearable and continuous monitoring. Since communication interfaces are the most energy-hungry parts of the sen sor nodes\, the &ldquo\;Near-Sensor Computing&rdquo\; concept aims at pre- processing the input data in order to keep only relevant information and t hus 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 circu its can be integrated into the hardware and interfaced as close as possibl e to the sensor.</span></p>\n<p><span style="font-size: 12pt\;">Seven exce llent speakers are scheduled to cover many aspects of integrated processin g and machine learning hardware\, including a distinguished lecturer from the IEEE Circuit and Systems Society. The application fields range from bi omedical signals (EEG\, ECG) to audio signals (silicon cochlea\, voice act ivity detection) to vision and general concepts of analog-to-feature conve rsion. The contributions will cover circuit-level\, system-level and integ ration challenges.</span></p>\n<p><span style="font-size: 12pt\;"><a href= "https://microelecsi.iemn.fr/wp-content/uploads/sites/6/2019/10/Annonce_Jo urnée_Machine-learning.pdf">LINK TO THE DETAILS (ABSTRACTS)</a></span></ p>\n<p><span style="font-size: 12pt\;"><a href="https://microelecsi.iemn.f r/wp-content/uploads/sites/6/2019/10/TechWS-V4.pdf">LINK TO THE POSTER</a> </span></p>\n<p><span style="font-size: 12pt\;"><strong>&nbsp\;</strong></ span></p>\n<p><span style="font-size: 12pt\;">&nbsp\;</span></p>\n<h3 styl e="color: #000000\; font-family: Verdana\,Arial\,Helvetica\,sans-serif\; f ont-size: 16.4px\; font-style: normal\; font-variant: normal\; font-weight : bold\; letter-spacing: normal\; orphans: 2\; text-align: left\; text-dec oration: none\; text-indent: 0px\; text-transform: none\; -webkit-text-str oke-width: 0px\; white-space: normal\; word-spacing: 0px\;"><span style="f ont-size: 12pt\;">&nbsp\;</span></h3><br /><br />Agenda: <br /><p><strong> Schedule :</strong></p>\n<p><strong>10h00 </strong><strong>&ndash\;</stron g><strong> 11h30: Jerald Yoo\, National University of Singapore\, IEEE CAS S Distinguished Lecturer</strong></p>\n<p>On-Chip Epilepsy Detection: Wher e Machine Learning Meets Wearable\, Patient-Specific Wearable Healthcare</ p>\n<p><strong>11h30 &ndash\; 12h30: Minhao Yang\, EPFL </strong></p>\n<p> Towards Near-Zero-Power Audio Inference Sensing</p>\n<p><strong>12h30 &nda sh\; 14h00: Lunch Break</strong></p>\n<p><strong>14h00 &ndash\; 14h30: Dee pu John\, UC Dublin</strong></p>\n<p>Low Power Sensor Design for Wearable Health Monitoring</p>\n<p><strong>14h30 &ndash\; 15h00: Benoit Larras\, IE MN\, Yncr&eacute\;a ISEN</strong></p>\n<p>Distributed Clique-Based Neural Networks for Data Fusion at the Edge</p>\n<p><strong>15h00 &ndash\; 15h30: Jean Martinet\, Universit&eacute\; C&ocirc\;te d'Azur\, I3S\, CNRS\, Poly tech Nice Sophia</strong></p>\n<p>Towards a Neuro-Inspired Machine Learnin g for Vision</p>\n<p><strong>15h30 &ndash\; 15h45: Coffee break</strong></ p>\n<p><strong>15h45 &ndash\; 16h15: S&eacute\;bastien Pecqueur\, IEMN</st rong></p>\n<p>Sensing Paradigms in a Neuromorphic Framework: What are the New Sensing Hardware Figure-of-Merits?</p>\n<p><strong>16h15 &ndash\; 16h4 5: Antoine Back\, LTCI\, T&eacute\;l&eacute\;com Paris\, Institut Polytech nique de Paris</strong></p>\n<p>Feature Selection Algorithms for the Desig n of a Flexible Analog-To-Feature Converter</p> END:VEVENT END:VCALENDAR