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SUMMARY:EE Distinguished Speakers Seminar: Perspectives of 2D materials fo
 r machine learning chips
DTSTART:20190329T131500
DTEND:20190329T140000
DTSTAMP:20260504T023150Z
UID:709447fff0e2fce1a3f89a5446c475350ea3dc2ca22fda93bc256ae4
CATEGORIES:Conferences - Seminars
DESCRIPTION:Giuseppe Iannaccone is Professor of electronics at the Univer
 sity of Pisa\, Italy\, Fellow of the Institute of Electrical and Electroni
 cs Engineers\, and Fellow of the American Physical Society.  His interest
 s include the fundamentals of transport and noise in nanoelectronic and m
 esoscopic devices\, the development of device modeling and TCAD tools\, an
 d the design of extremely low-power circuits and systems for RFID and ambi
 ent intelligence scenarios. He has published more than 200 papers in peer-
 reviewed journals and more than 130 papers in proceedings of international
  conferences. Giuseppe Iannaccone has coordinated a few European and Natio
 nal Projects involving multiple partners and has acted as the Principal In
 vestigator in several research projects funded by European and National pu
 blic agencies and by private organizations. He is presently coordinating t
 he QUEFORMAL FET h2020 project\, Quantum Engineering for Machine Learning.
  Find more on him on www.iannaccone.org\nAbstract: In this talk we will 
 discuss the challenges\, opportunities\, and the performance potential of 
 atomistic engineering of electron devices exploiting the fundamental prope
 rties of 2D material heterostructures\, with a particular attention to com
 puter architectures for machine learning applications.\nThe “materials-o
 n-demand paradigm” based on the 2D materials is a modern evolution of wh
 at in the 1980s was called “band-gap engineering” or “band-structure
  engineering”\, i.e.\, the artificial modification of band edge profile
 s using heterostructures made possible by epitaxial growth of III-V and II
 -VI material systems.\nLateral and vertical heterostructures of 2D materia
 ls could represent a revolutionary and enabling technology to device engin
 eering providing the possibility to engineer transistors and memory at the
  atomistic scale\, which we like to call “quantum engineering”.\nWe wi
 ll show that the challenge of equipping devices at the edge of the cloud w
 ith cognitive capabilities requires dedicated machine learning chips and i
 nnovation in architectures\, circuits and technology\, for which heterostr
 uctures of 2D materials appear to be particularly well suited.\n 
LOCATION:ELA 2 https://plan.epfl.ch/?room==ELA%202
STATUS:CONFIRMED
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