Faculty of Information Technology, BUT

Course details

Evolutionary and neural hardware

EUD Acad. year 2019/2020 Summer semester

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 for 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, DNA computing and approximate computing. Typical applications will illustrate these approaches..

Guarantor

Language of instruction

Czech

Completion

Examination (oral)

Time span

26 hrs lectures

Assessment points

100 exam

Department

Lecturer

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.

Generic learning outcomes and competences

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
  • Sekanina L., Vašíček Z., Růžička R., Bidlo M., Jaroš J., Švenda P.: Evoluční hardware: Od automatického generování patentovatelných invencí k sebemodifikujícím se strojům. Academia Praha 2009, ISBN 978-80-200-1729-1 

Fundamental 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
  • Sekanina L., Vašíček Z., Růžička R., Bidlo M., Jaroš J., Švenda P.: Evoluční hardware: Od automatického generování patentovatelných invencí k sebemodifikujícím se strojům. Academia Praha 2009, ISBN 978-80-200-1729-1 

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 analogue 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. DNA computing
  13. Recent HW/SW platforms and applications

Syllabus - others, projects and individual work of students

A project will be assigned to each student. 

Controlled instruction

Elaboration and presentation of a project.

Course inclusion in study plans

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