Course detail

Evolutionary and neural hardware

FIT-EUDAcad. year: 2019/2020

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..

Learning outcomes of the course unit

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.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

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 (http://www.academia.cz/evolucni-hardware.html). Academia Praha 2009, ISBN 978-80-200-1729-1

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Language of instruction

Czech

Work placements

Not applicable.

Aims

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

Specification of controlled education, way of implementation and compensation for absences

Elaboration and presentation of a project.

Classification of course in study plans

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

  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