Course details

Evolutionary and Neural Hardware

EUD Acad. year 2020/2021 Summer semester

Current academic year

This course introduces selected computational models and computer systems which have appeared at the intersection of hardware and artificial intelligence in order to address insufficient performance and energy efficiency of conventional computers in solving some hard problems. The course surveys relevant theoretical models, circuit techniques and computational intelligence methods inspired in biology. In particular, the following topics will be discussed: evolutionary design, evolvable hardware, neural hardware, neuroevolution and approximate computing. Typical applications will illustrate these approaches.

Doctoral state exam - topics:

  1. Inspiration in biology (adaptation, self-organization, entropy, evolution, learning).
  2. Hardware and reconfigurable devices for artificial intelligence.
  3. Cartesian genetic programming.
  4. Scalability issues of evolutionary circuit design and their solutions.
  5. Evolutionary design of analog circuits.
  6. Cellular automata in 1D and 2D, Wolfram classes, self-replication.
  7. Approximate computing (principles, error metrics, circuit approximation methods).
  8. Deep neural networks.
  9. Hardware implementation of neural networks.
  10. Neuroevolution.

Guarantor

Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)

Language of instruction

Czech, English

Completion

Examination (oral)

Time span

  • 26 hrs lectures

Assessment points

  • 100 pts final exam

Department

Department of Computer Systems (UPSY)

Lecturer

Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)

Instructor

Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)

Subject specific learning outcomes and competences

Students will be able to utilize evolutionary algorithms to design electronic circuits. They will be able to model, simulate and implement bio-inspired computational systems, particularly evolvable and neural hardware.
Understanding the relation between computers (computing) and some natural processes.

Learning objectives

To understand the principles of bio-inspired computing techniques and their use particularly during the design, hardware implementation and operation of computer systems.

Study literature

  • Floreano, D., Mattiussi, C.: Bioinspired Artificial Intelligence: Theories, Methods, and Technologies. The MIT Press, Cambridge 2008, ISBN 978-0-262-06271-8
  • Trefzer M., Tyrrell A.M.: Evolvable Hardware - From Practice to Application. Berlin: Springer Verlag, 2015, ISBN 978-3-662-44615-7
  • Reda S., Shafique M.: Approximate Circuits - Methodologies and CAD. Springer Nature, 2019, ISBN 978-3-319-99322-5

Syllabus of lectures

  1. Introduction.
  2. Bio-inspired computational models (inspiration, principles of adaptation and self-organization).             
  3. Approximate computing and energy efficiency.
  4. Hardware and reconfigurable devices for artificial intelligence.
  5. Evolutionary design.
  6. Cartesian genetic programming.
  7. Evolutionary design of digital and analog circuits.
  8. Scalability problems of evolutionary design.
  9. Computational development, cellular automata, L-systems.
  10. Deep neural networks and their hardware implementation.
  11. Approximate computing for neural networks.
  12. Neuroevolution.
  13. Recent HW/SW platforms and applications.

Progress assessment

Submission of the project on time, exam.

Controlled instruction

During the course, it is necessary to submit the project and pass the exam. Teaching is performed as lectures or controlled self-study; the missed classes need to be replaced by self-study.

Course inclusion in study plans

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