CASS Talks with Enrico Macii and Gianvito Urgese, Politecnico di Torino, Italy

#CASS #Talks #2021

Abstract:

The Industry 4.0 paradigm relies on the correct implementation of a digital thread infrastructure that allows the collection, storage and analysis of data throughout the lifecycle of the system under production for providing to the different stakeholders the right data to the right place at the right time.  The trend is to centralize the collection of raw data in cloud solutions which executes the analysis and returns 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 increasing; therefore, the pure cloud paradigm would not be scalable and efficient 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 consumption constraints and limited computational capability. In this challenge, neuromorphic computational paradigms and hardware architectures, inspired by the working principles of the most advanced, reliable, and power-efficient sensor data analytics system — the human brain — could be considered key players. Neuromorphic engineering opens for a huge solution space to be explored that calls for the definition of standardized computational primitives, frameworks, and software tools needed for building neuromorphic applications that can be combined with digital technology and deployed in industrial application scenarios. This talk will explore some of these challenges and discuss some solutions supported by case-study examples.

Short Bio: 

Enrico Macii is a Full Professor of Computer Engineering at Politecnico di Torino, Torino, Italy. He holds a Laurea Degree in Electrical Engineering from Politecnico di Torino (1990), a Laurea Degree in Computer Science from Università di Torino (1991) and a PhD degree in Computer Engineering from Politecnico di Torino (1995).  His research interests are in the design of electronic digital circuits and systems, with particular emphasis on low power consumption aspects. In the last decade, he has been growingly involved in projects focusing on the development of new technologies, methodologies and policies for achieving energy efficiency in buildings, districts and cities, sustainable urban mobility, and clean and intelligent manufacturing. In the fields above, he has authored over 600 scientific publications. Enrico Macii is a Fellow of the IEEE. 

Gianvito Urgese is Assistant Professor at Politecnico di Torino (Italy) where he received a PhD in Computer and Systems Engineering in 2016. He received his M.Sc. degree (summa cum laude) in Electrical Engineering at Politecnico di Torino. His main research interests are neuromorphic engineering, parallel and heterogeneous architectures, algorithm optimization 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.