BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Sao_Paulo BEGIN:DAYLIGHT DTSTART:20380119T001407 TZOFFSETFROM:-0300 TZOFFSETTO:-0300 RRULE:FREQ=YEARLY;BYDAY=3TU;BYMONTH=1 TZNAME:-03 END:DAYLIGHT BEGIN:STANDARD DTSTART:20190216T230000 TZOFFSETFROM:-0200 TZOFFSETTO:-0300 RRULE:FREQ=YEARLY;BYDAY=3SA;BYMONTH=2 TZNAME:-03 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20211222T134409Z UID:316212E2-5827-48EA-9E7B-A7BAAC00C3FA DTSTART;TZID=America/Sao_Paulo:20211022T133000 DTEND;TZID=America/Sao_Paulo:20211022T153000 DESCRIPTION:Abstract:\n\nThe Industry 4.0 paradigm relies on the correct im plementation of a digital thread infrastructure that allows the collection \, storage and analysis of data throughout the lifecycle of the system und er production for providing to the different stakeholders the right data t o the right place at the right time. The trend is to centralize the collec tion of raw data in cloud solutions which executes the analysis and return s the response to the device on the field. However\, with the increase of the number of smart connected devices adopted\, the amount of data is incr easing\; therefore\, the pure cloud paradigm would not be scalable and eff icient for advanced monitoring and control tasks that require low-latency local computing solutions. The shift of computation from the cloud to the edge calls for more advanced edge devices that often struggle with power c onsumption constraints and limited computational capability. In this chall enge\, neuromorphic computational paradigms and hardware architectures\, i nspired by the working principles of the most advanced\, reliable\, and po wer-efficient sensor data analytics system — the human brain — could b e considered key players. Neuromorphic engineering opens for a huge soluti on space to be explored that calls for the definition of standardized comp utational primitives\, frameworks\, and software tools needed for building neuromorphic applications that can be combined with digital technology an d deployed in industrial application scenarios. This talk will explore som e of these challenges and discuss some solutions supported by case-study e xamples.\n\nShort Bio:\n\nEnrico Macii is a Full Professor of Computer Eng ineering at Politecnico di Torino\, Torino\, Italy. He holds a Laurea Degr ee in Electrical Engineering from Politecnico di Torino (1990)\, a Laurea Degree in Computer Science from Università di Torino (1991) and a PhD deg ree in Computer Engineering from Politecnico di Torino (1995). His researc h interests are in the design of electronic digital circuits and systems\, with particular emphasis on low power consumption aspects. In the last de cade\, he has been growingly involved in projects focusing on the developm ent of new technologies\, methodologies and policies for achieving energy efficiency in buildings\, districts and cities\, sustainable urban mobilit y\, and clean and intelligent manufacturing. In the fields above\, he has authored over 600 scientific publications. Enrico Macii is a Fellow of the IEEE.\n\nGianvito Urgese is Assistant Professor at Politecnico di Torino (Italy) where he received a PhD in Computer and Systems Engineering in 201 6. He received his M.Sc. degree (summa cum laude) in Electrical Engineerin g at Politecnico di Torino. His main research interests are neuromorphic e ngineering\, parallel and heterogeneous architectures\, algorithm optimiza tion in bioinformatics\, embedded systems\, and tool-chains integration in the industrial domain. In the fields above he has authored 40 scientific publications. He is a Member of the IEEE.\n\nVirtual: https://events.vtool s.ieee.org/m/280068 LOCATION:Virtual: https://events.vtools.ieee.org/m/280068 ORGANIZER:jose.azambuja@inf.ufrgs.br SEQUENCE:3 SUMMARY:CASS Talks with Enrico Macii and Gianvito Urgese\, Politecnico di T orino\, Italy URL;VALUE=URI:https://events.vtools.ieee.org/m/280068 X-ALT-DESC:Description: <br /><p>&nbsp\;</p>\n<p>&nbsp\;</p>\n<p><span styl e="color: #000000\;">Abstract:</span></p>\n<p>The Industry 4.0 paradigm re lies on the correct implementation of a digital thread infrastructure that &nbsp\;allows the collection\, storage and analysis of data throughout the lifecycle of the system under&nbsp\;production for providing to the diffe rent stakeholders the right data to the right place at the right&nbsp\;tim e.&nbsp\;&nbsp\;The trend is to centralize the collection of raw data in c loud solutions which executes the&nbsp\;analysis and returns the response to the device on the field. However\, with the increase of the number&nbsp \;of smart connected devices adopted\, the amount of data is increasing\; therefore\, the pure cloud&nbsp\;paradigm would not be scalable and effici ent for advanced monitoring and control tasks that require&nbsp\;low-laten cy local computing solutions. The shift of computation from the cloud to t he edge calls for&nbsp\;more advanced edge devices that often struggle wit h power consumption constraints and limited&nbsp\;computational capability . In this challenge\, neuromorphic computational paradigms and hardware&nb sp\;architectures\, inspired by the working principles of the most advance d\, reliable\, and power-efficient&nbsp\;sensor data analytics system &mda sh\; the human brain &mdash\; could be considered key players. Neuromorphi c&nbsp\;engineering opens for a huge solution space to be explored that ca lls for the definition of standardized&nbsp\;computational primitives\, fr ameworks\, and software tools needed for building neuromorphic&nbsp\;appli cations that can be combined with digital technology and deployed in indus trial application&nbsp\;scenarios. This talk will explore some of these ch allenges and discuss some solutions supported by&nbsp\;case-study examples .</p>\n<p>Short Bio:&nbsp\;</p>\n<p>Enrico Macii&nbsp\;is a Full Professor of Computer Engineering at Politecnico di Torino\, Torino\, Italy. He hol ds&nbsp\;a Laurea Degree in Electrical Engineering from Politecnico di Tor ino (1990)\, a Laurea Degree in&nbsp\;Computer Science from Universit&agra ve\; di Torino (1991) and a PhD degree in Computer Engineering from&nbsp\; Politecnico di Torino (1995).&nbsp\;&nbsp\;His research interests are in t he design of electronic digital circuits and&nbsp\;systems\, with particul ar emphasis on low power consumption aspects. In the last decade\, he has been&nbsp\;growingly involved in projects focusing on the development of n ew technologies\, methodologies and&nbsp\;policies for achieving energy ef ficiency in buildings\, districts and cities\, sustainable urban mobility\ , and&nbsp\;clean and intelligent manufacturing. In the fields above\, he has authored over 600 scientific publications.&nbsp\;Enrico Macii is a Fel low of the IEEE.&nbsp\;<br /><br />Gianvito Urgese&nbsp\;is Assistant Prof essor at Politecnico di Torino (Italy) where he received a PhD in&nbsp\;Co mputer and Systems Engineering in 2016. He received his M.Sc. degree (summ a cum laude) in&nbsp\;Electrical Engineering at Politecnico di Torino. His main research interests are neuromorphic&nbsp\;engineering\, parallel and heterogeneous architectures\, algorithm optimization in bioinformatics\,& nbsp\;embedded systems\, and tool-chains integration in the industrial dom ain. In the fields above he has&nbsp\;authored 40 scientific publications. &nbsp\;He is a Member of the IEEE.</p> END:VEVENT END:VCALENDAR