Seasonal School on

Efficient Circuits and Systems for Brain-Inspired Computing

8-9th September 2022

 

This seasonal school (eCAS-BIC) targets the study of circuits and systems that seek to improve the efficiency of current brain-inspired architectures. In this educational event, basic and advanced issues on hardware for brain-inspired computing will be presented targeting students, professionals and scientists. 

The eCAS-BIC Seasonal School will cover the following topics:

  • Architectures for energy-efficient Deep Learning (DL)
  • Edge Artificial Intelligence (AI) 
  • Advanced circuits for neuromorphic computing / analog and mixed signal integrated circuit design with application to neuromorphic systems
  • Emerging technologies for brain-inspired computing
  • Biomedical applications

 

Agenda

  Day 1 - September 8th 2022 Day 2 - September 9th 2022
8:45  Opening  
  Neuromorphic Computing  Edge Artificial Intelligence
 9:00 - 10:00

- Michael Hopkins (U. Manchester)

The SpiNNaker neuromorphic architecture and its application to a novel learning algorithm (abstract)

- Michele Rossi (U. Padova)

Energy Efficient Scheduling at the Network Edge: Current Solutions and Future Research Pathways (abstract)

10:00-11:00

- Bernabé Linares (CNM-Seville)

Event-driven convolution based processing (abstract)

 

- Marco Miozzo (CTTC)

Taming the Environmental Impact of AI with Collaborative and Distributed Learning at the Edge (abstract)

11:00-11:30  Coffee Break  Coffee break
    Emerging technologies for brain-inspired computing  Applications
11:30-12:30

- Daniele Ielmini (Politecnico di Milano)

Emerging resistive switching devices for neuromorphic sensing and learning (abstract)

- David Atienza (EPFL)

Edge AI Biomedical Systems for a Privacy-Preserving IoT Era (abstract)

12:30-13:30

- Alon Ascoli (TU Dresden)

Locally-Active Memristors Allow to Reproduce High-Order Biological Phenomena through Low-Order Bio-Inspired Dynamic Arrays (abstract)

- Josep Maria Margarit (CNM- Barcelona)

Pushing the Analytical Limits through Fully-Integrated  Electrochemical Sensing and Neuro-Inspired Perception (abstract)

13:30-15:00   Lunch Break   Lunch Break
15:00-16:00

- Catherine Schuman (U. Tennessee)

 Algorithms for Neuromorphic Computing and Neural Hardware (abstract)

 
16:30 - 18:30

- Luis Hernández (UPM)

Hands-on Deep Learning with Keras and Tensorflow (abstract)

 

 

Registration

The school is conceived for Master and PhD Students in the Electrical Engineering and Computer Science fields. Registration is free, but unfortunately we have reached the masimum number of attendees (50). If still interested, please send an email to brain.inspired.cas.school@gmail.com to be included in the waiting list.

The registration in the school includes:

  • Attendance to the lectures.
  • Electronic documentation.
  • Meals.
  • 12-hour Course certificate 

Travel grants

Up to 20 travel grants will be granted by the organization. In order to apply for a travel grant you must register before July 31st 2022. Mark the application while registering.

Venue

The school will be hosted by the Universidad Politécnica de Madrid (UPM) at the Escuela Técnica Superior de Ingenieros de Telecomunicación (ETSIT), which is located in "Ciudad Universitaria", a university campus in the west part of Madrid. 

The address of ETSIT-UPM is:

Escuela Técnica Superior de Ingenieros de Telecomunicación
Avenida Complutense s/n
Ciudad Universitaria
28040 Madrid
Spain

Local transport

ETSI Telecomunicación can be reached by bus, underground, or taxi.  Bus line F comes from "Cuatro Caminos" and bus line 82 comes from "Moncloa". Both stop at ETSIT's entrance when coming from Cuatro Caminos or Moncloa. 

Metro tickets can be bought at underground stations. The underground station "Ciudad Universitaria", line 6, is the closest to ETSIT (15-min walk approximately). Cuatro Caminos and Moncloa also have stations in the same line. Very close to the "Ciudad Universitaria" station you can connect with bus lines 82G or U, which stop at ETSIT's entrance.

Organization

The school has been organized by the  IEEE-CASS-Spain Chapter. Organizing committee is composed of:

  • Francesc Moll (Universitat Politècnica de Catalunya)
  • Marisa López-Vallejo (Universidad Politécnica de Madrid)

Local arrangements are carried out by Eva Rojas.

Speakers

  • Alon Ascoli, TU Dresden
  • David Atienza, EPFL
  • Luis Hernández, UPM
  • Michael Hopkins, University of Manchester
  • Daniele Ielmini, Politécnico di Milano
  • Bernabé Linares, CNM Seville
  • Josep Maria Margarit, CNM Barcelona
  • Marco Miozzo, CTTC
  • Michele Rossi, Università di Padova
  • Catherine Schuman, University of Tennessee

Alon Ascoli, TU Dresden

Alon Ascoli (IEEE Senior Member) received a Habilitation as Full Professor in Nonlinear Circuit Theory from Technische Universität Dresden (TU Dresden) in 2022, a Ph.D. Degree in Electronic Engineering from University College Dublin in 2006, and a First-Class Honours Master's Degree in Electronic Engineering from Universita’ degli Studi Roma Tre in 2001. He currently holds a tenure faculty position at the Institute of Principles of Electrical and Electronic Engineering of TU Dresden. He develops system-theoretic methods for the analysis and design of bio-inspired memristive/memcapacitive
circuits bound to enable progress in electronics beyond the Moore era, and to allow the plausible reproduction of complex phenomena emerging in biological systems. He was honoured with Best Paper Awards from IJCTA in 2007 and MOCAST in 2020. In April 2017 he was conferred the habilitation title as Associate Professor in Electrical Circuit Theory from the Italian Ministry of Education. He is a member of the Scientific Advisory Board of the Chua Memristor Center, and of the IEEE Nanoelectronics and Gigascale Systems Technical Committee (Nano-Giga TC). He was the Chair of the IEEE Cellular Nonlinear Networks and Array Computing (CNNAC) TC from 2019 to 2021. Since 2021 he is the Chair of the new-born IEEE Cellular Nonlinear Networks and Memristive Array Computing (CNN-MAC) TC.

David Atienza, EP

David Atienza is Full Professor of Electrical and Computer Engineering and leads the
Embedded Systems Laboratory (ESL) at EPFL, Switzerland. He received his MSc and PhD
degrees in Computer Science and Engineering from UCM (Spain) and IMEC (Belgium). His
research interests focus on system-level design methodologies for energy-efficient multi-
processor system-on-chip architectures (MPSoC) and next-generation smart embedded
systems (particularly wearables) for the Internet of Things (IoT) era. In these fields, he is co-author of more than 350 publications, 14 patents, and received several best paper awards in top conferences. He also was the Technical Program Chair of DATE 2015 and General Chair of DATE 2017. Dr. Atienza received the DAC Under-40 Innovators Award in 2018, IEEE TCCPS Mid-Career Award in 2018, an ERC Consolidator Grant in 2016, the IEEE CEDA Early Career Award in 2013, the ACM SIGDA Outstanding New Faculty Award in 2012, and a Faculty Award from Sun Labs at Oracle in 2011. He is an IEEE Fellow and an ACM Distinguished Member, as well as Chair of EDAA for the period 2022-2023.

Michael Hopkins, U. of Manchester

Michael Hopkins first trained in acoustics and sound recording techniques and then in 1988 took a BSc(Hons) in Computing & IT at the University of Surrey with a focus on numerical software engineering and probabilistic methods. From 1993 he ran two consultancies over 20 years specialising in probabilistic modelling of complex and noisy engineering systems using optimal experimental design, high-dimensional global optimisation methods and Bayesian inference. In 2013 he joined the SpiNNaker group at the University of Manchester where he is a Research Fellow with an interest in spiking neural networks, applied information theory, reduced-precision computation and how dendritic computation and local homeostatic adaptation rules may allow single-shot learning and higher-level cognitive abilities to emerge. Contact: michael.hopkins@manchester.ac.uk.

Luis Hernández

Luis A. Hernández Gómez received the Telecommunication Engineer degree and the Ph.D. degree in Telecommunication from the Universidad Politécnica de Madrid, Spain, in 1982 and 1988 respectively.
He has been on ETSI Telecomunicaciones - Universidad Politécnica de Madrid since 1987
where he is currently Associate Professor with the Departamento de Señales Sistemas y
Radiocomunicaciones. Since 1994 he has been collaborating as Scientific Consultant with Telefónica Investigación y Desarrollo in Speech Technologies, Data Analysis and Machine Learning applications. He coordinates the Signal Processing and Machine Learning for Big Data Master Program at ETSIT-UPM. His current interests include research on deep learning models for speech, audio, music, natural language processing, and sensor data applications. He has directed several doctoral theses in these areas, has written numerous articles and has participated in different public and private research projects.

Daniele Ielmini, Politecnico di Milano

Daniele Ielmini is a Professor at the Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Italy. He received the Ph.D. from Politecnico di Milano in 2000. He held visiting positions at Intel Corporation and Stanford University in 2006. His research interests include the modeling and characterization of non-volatile memories, such as phase change memory (PCM), resistive switching memory (RRAM), and spin-transfer torque magnetic memory (STT-MRAM). He authored/coauthored more than 300 papers in international journals and conferences. He is Associate Editor of IEEE Trans. Nanotechnology and Semiconductor Science and Technology (IOP). He received the Intel Outstanding Researcher Award in 2013, the ERC Consolidator Grant in 2014, the IEEE-EDS Paul Rappaport Award in 2015 and the ERC Advanced Grant in 2021. He is a Fellow of the IEEE.

Bernabé Linares, CNM Seville

Bernabé Linares-Barranco received a first Ph.D. degree in high-frequency OTA-C oscillator design in June 1990 from the University of Seville, Spain, and a second Ph.D deegree in analog neural network design in December 1991 from Texas A&M University , College-Station, USA. Since June 1991, he has been a Tenured Scientist at the "Instituto de Microelectrónica deSevilla" , (IMSE-CNM-CSIC) Sevilla , Spain , which since 2015 is a Mixed Center between the University of Sevilla and the Spanish Research Council (CSIC). From September 1996 to August 1997, he was on sabbatical stay at the Department of Electrical and Computer Engineering of the Johns Hopkins University . During Spring 2002 he was Visiting Associate Professor at the Electrical Engineering Department of Texas A&M University , College-Station, USA. In January 2003 he was promoted to Tenured Researcher, and in January 2004 to Full Professor. Since February 2018, he is the Director of the "Insitituto de Microelectrónica de Sevilla".

He has been involved with circuit design for telecommunication circuits, VLSI emulators of biological neurons, VLSI neural based pattern recognition systems, hearing aids, precision circuit design for instrumentation equipment, VLSI transistor mismatch parameters characterization, and over the past 20 years has been deeply involved with neuromorphic spiking circuits and systems, with strong emphasis on vision and exploiting nanoscale memristive devices for learning. He is co-founder of two start-ups, Prophesee SA (www.prophesee.ai) and GrAI-Matter-Labs SAS (www.graimatterlabs.ai), both on neuromorphic hardware.

Dr. Linares-Barranco was corecipient of the 1997 IEEE Trans. on VLSI Systems Best Paper Award for the paper "A Real-Time Clustering Microchip Neural Engine", and of the 2000 IEEE Trans.on Circuits and Systems Darlington Award for the paper "A General Translinear Principle for Subthreshold MOS Transistors". He has been Associate Editor of the IEEE Trans. on Circuits and Systems Part II, and IEEE Transactions on Neural Networks. Since April 2010 he is Associate Editor for "Frontiers in Neuromorphic Engineering", as part of the open access "Frontiers in Neuroscience" journal series. Since Jan. 2021 he is Specialty Chief Editor of "Frontiers in Neuromorphic Engineering".

He was Chief Guest Editor of the IEEE Transactions on Neural Networks Special Issue on 'Hardware Neural Networks Implementations '. He is an IEEE Fellow since January 2010. He is listed among the Stanford top 2% most world-wide cited scientist in Electrical and Electronic Engineering.

Josep Maria Margarit, CNM Barcelona

Josep Maria Margarit-Taulé received his Ph.D. degree in Electronic Engineering from the Universitat Politécnica de Catalunya, Barcelona, Spain, in 2015. He has been a Postdoctoral Fellow at the Institute of Neuroinformatics, ETH and University of Zurich, and an Associate Lecturer Professor with the Microelectronics and Electronic Systems Department at the Universitat Autónoma de Barcelona. Currently, he is a Research Fellow at the Instituto de Microelectrónica de Barcelona, IMB-CNM, CSIC. His research activity includes bioinspired deep-learning algorithms and energy-efficient miniaturized circuits to integrate intelligent and portable sensing devices. Dr. Margarit is a recipient of the Marie Skłodowska-Curie Individual Fellowship (2017), the Springer Theses Award (2016), the BE-DGR Outgoing Research Fellowship (2011), the TEM-DGR Industrial Research Fellowship (2009), and the 2008 Best Paper Award by the IEEE CAS Sensory Systems Technical Committee. He is a member of the editorial board of Frontiers in Neuroscience, the IEEE CAS Sensory Systems Technical Committee, the European ICT4Water cluster, the European AI Alliance, and the Intel Neuromorphic Research Community.

Marco Miozzo, CTTC

Marco Miozzo received his M.Sc. degree in Telecommunication Engineering from the University of Ferrara (Italy) in 2005 and the Ph.D. from the Technical University of Catalonia (UPC) in 2018. In June 2008 he joined the Centre Tecnologic de Telecomunicacions de Catalunya (CTTC). In CTTC he has been involved in several EU founded projects. He participated in several R&D projects, among them SCAVENGE, 5G-Crosshaul, Flex5Gware and SANSA, working on environmental sustainable mobile networks with energy harvesting capabilities through learning techniques. Currently he is collaborating with the EU founded H2020 GREENEDGE (MSCA ETN) and SONATA (CHIST-ERA). His main research interests are: sustainable mobile networks, green wireless networking, energy harvesting, multi-agent systems, machine learning, green AI, explainable AI.

Michele Rossi

Michele Rossi (IEEE Senior Member) is Full Professor in the Department of Information Engineering (DEI) at the University of Padova (UNIPD), Italy, where is the head of the Master's Degree in ICT for internet and Multimedia (http://mime.dei.unipd.it/). He
also teaches Human Data Analysis at the Data Science Master's degree at the Department of Mathematics (DM) at the same university. Since 2017, he has been the Director of the DEI/IEEE Summer School of Information Engineering. His research interests lie broadly in wireless sensing systems (including joint communication and sensing and radar networks), green mobile networks, edge and wearable computing. Over the years, he has been involved in several EU projects on wireless sensing and IoT, and has collaborated with major companies such as Ericsson, DOCOMO, Samsung and INTEL. His research is currently supported by the European Commission through the H2020 projects MINTS (grant no. 861222) on ``mmWave networking and sensing'' and GREENEDGE (grant no. 953775) on ``green edge computing for
mobile networks'' (project coordinator). Dr. Rossi has been the recipient of seven best paper awards from the IEEE and currently serves on the Editorial Boards of the IEEE Transactions on Mobile Computing, and of the Open Journal of the Communications Society. For further info on current research and activities, see: http://www.dei.unipd.it/~rossi/

Catherine Schuman, U. Tennessee

Catherine (Katie) Schuman is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee (UT). She received her Ph.D. in Computer Science from UT in 2015, where she completed her dissertation on the use of evolutionary algorithms to train spiking neural networks for neuromorphic systems. Katie previously served as a research scientist at Oak Ridge National Laboratory, where her research focused on algorithms and applications of neuromorphic systems.  Katie co-leads the TENNLab Neuromorphic Computing Research Group at UT.  She has over 70 publications as well as seven patents in the field of neuromorphic computing. She received the Department of Energy Early Career Award in 2019.