ELE 523E

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As current CMOS based technologies are approaching their anticipated limits, emerging nanotechnologies and new computing paradigms are expected to be used in future electronic circuits. This course overviews  nanoelectronic circuits in a comparison with those of conventional CMOS-based. Deterministic and probobalistic emerging computing models as well as related algorithms and CAD tools are investigated. Regarding the interdisciplinary nature of emerging technologies, this course is appropriate for graduate students in different majors including electronics engineering, control engineering, computer science, applied physics, and mathematics. No prior course is required; only basic (college-level) knowledge in circuit design and mathematics is assumed. Topics that are covered include:
 
As current CMOS based technologies are approaching their anticipated limits, emerging nanotechnologies and new computing paradigms are expected to be used in future electronic circuits. This course overviews  nanoelectronic circuits in a comparison with those of conventional CMOS-based. Deterministic and probobalistic emerging computing models as well as related algorithms and CAD tools are investigated. Regarding the interdisciplinary nature of emerging technologies, this course is appropriate for graduate students in different majors including electronics engineering, control engineering, computer science, applied physics, and mathematics. No prior course is required; only basic (college-level) knowledge in circuit design and mathematics is assumed. Topics that are covered include:
  
−
* Circuit elements and devices in computational nanoelectronics (in comparison with CMOS) including nano-crossbar switches, reversible quantum gates, approximate circuits and systems, and emerging transistors.  
+
* Circuit elements and devices in computational nanoelectronics (in comparison with CMOS) including nano-crossbar and memristor switches, reversible quantum gates, approximate circuits and systems, and emerging transistors.  
 
* Introduction of emerging computing models and algorithms in circuit level.
 
* Introduction of emerging computing models and algorithms in circuit level.
 
* Analysis and synthesis of deterministic and probabilistic computing paradigms.
 
* Analysis and synthesis of deterministic and probabilistic computing paradigms.

Revision as of 10:15, 17 September 2018

Contents

Announcements

  • Sept. 17th The course is given in the Bedri Karafakioğlu seminar room (third floor), EEF.

Overview

As current CMOS based technologies are approaching their anticipated limits, emerging nanotechnologies and new computing paradigms are expected to be used in future electronic circuits. This course overviews nanoelectronic circuits in a comparison with those of conventional CMOS-based. Deterministic and probobalistic emerging computing models as well as related algorithms and CAD tools are investigated. Regarding the interdisciplinary nature of emerging technologies, this course is appropriate for graduate students in different majors including electronics engineering, control engineering, computer science, applied physics, and mathematics. No prior course is required; only basic (college-level) knowledge in circuit design and mathematics is assumed. Topics that are covered include:

  • Circuit elements and devices in computational nanoelectronics (in comparison with CMOS) including nano-crossbar and memristor switches, reversible quantum gates, approximate circuits and systems, and emerging transistors.
  • Introduction of emerging computing models and algorithms in circuit level.
  • Analysis and synthesis of deterministic and probabilistic computing paradigms.
  • Performance of the computing models regarding area, power, speed, and accuracy.
  • Uncertainty and faults: fault analysis and tolerance techniques for permanent and transient faults.

Syllabus

ELE 523E: Computational Nanoelectronics, CRN: 14785, Mondays 13:30-16:30, Room: Z2 (Ground Floor-EEF), Fall 2018.
Instructor

Mustafa Altun

  • Email: altunmus@itu.edu.tr
  • Tel: 02122856635
  • Office hours: 14:00 – 15:00 on Wednesdays in Room:3005, EEF (or stop by my office any time)
Grading
  • Homework: 20%
    • 4 homeworks (5% each)
  • Midterm Exam: 20%
    • The midterm is during the lecture time on 5/12/2016.
  • Presentation: 20%
    • Presentations are made individually or in groups depending on class size.
    • Presentation topics will be posted.
  • Final Project: 40%
Reference Books
  • Adamatzky, A. (Ed.). (2016). Advances in Unconventional Computing: Volume 1: Theory (Vol. 22). Springer.
  • Waser, R. (2012). Nanoelectronics and information technology. John Wiley & Sons.
  • Iniewski, K. (2010). Nanoelectronics: nanowires, molecular electronics, and nanodevices. McGraw Hill Professional.
  • Stanisavljević, M., Schmid, M, Leblebici, Y. (2010). Reliability of Nanoscale Circuits and Systems: Methodologies and Circuit Architectures, Springer.
  • Adamatzky, A., Bull, L., Costello, B. L., Stepney, S., Teuscher, C. (2007). Unconventional Computing, Luniver Press.
  • Zomaya, Y. (2006). Handbook of Nature-Inspired and Innovative Computing: Integrating Classical Models with Emerging Technologies, Springer.
  • Yanushkevich, S., Shmerko, V., Lyshevski, S. (2005). Logic Design of NanoICs, CRC Press.
Policies
  • Homeworks are due at the beginning of class. Late homeworks will be downgraded by 20% for each day passed the due date.
  • Collaboration is permitted and encouraged for homeworks, but each collaborator should turn in his/her own answers.
  • The midterm is in closed-notes and closed-books format.
  • Collaboration is not permitted for the final project.

Weekly Course Plan

Date
Topic
Week 1, 17/9/2018 Introduction
Week 2, 24/9/2018 Overview of emerging nanoscale devices and switches
Week 3, 1/10/2018 Reversible quantum computing, reversible circuit analysis and synthesis
Weeks 4, 8/10/2018 Molecular computing with individual molecules and DNA strand displacement
Weeks 5, 15/10/2018 Computing and logic synthesis with switching nano arrays
Week 6, 22/10/2018 Probabilistic/Stochastic computing with random bit streams and probabilistic switches
Weeks 7, 29/10/2018 HOLIDAY, no class
Week 8, 5/11/2018 HOLIDAY, no class
Week 9, 12/11/2018 Approximate computing and Bayesian networks
Weeks 10, 19/11/2018 Defects, faults, errors, and their analysis
Week 11, 26/11/2018 Permanent and transient (concurrent) fault tolerance: error detecting and correcting
Week 12, 3/12/2018 MIDTERM
Weeks 13, 10/12/2018 Overview of the midterm, presentation schedule, and final project
Weeks 14, 17/12/2018 Student presentations
Weeks 15, 24/12/2018 Student presentations

Course Materials

Lecture Slides Lecture Slides Homeworks Presentations & Exams & Projects
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