BIN Acad. year 2023/2024 Summer semester 5 credits
This course introduces computational models and computers which have appeared at the intersection of hardware and artificial intelligence in the recent years as an attempt to solve computational and energy inefficiency of conventional computers. The course surveys relevant theoretical models, reconfigurable architectures and computational intelligence techniques inspired at the levels of phylogeny, ontogeny and epigenesis. In particular, the following topics will be discussed: emergence and self-organization, evolutionary design, evolvable hardware, cellular systems, neural hardware, molecular computers and nanotechnology. Typical applications will illustrate the mentioned approaches.
Language of instruction
- 26 hrs lectures
- 8 hrs pc labs
- 18 hrs projects
- 52 pts final exam (written part)
- 15 pts mid-term test (written part)
- 8 pts labs
- 25 pts projects
Subject specific learning outcomes and competences
Students will be able to utilize evolutionary algorithms to design computational structures and electronic circuits. They will be able to model, simulate and implement non-conventional, in particular bio-inspired, computational systems.
Understanding the relation between computers (computing) and some natural processes.
To understand the principles of bio-inspired computational systems. To be able to use the bio-inspired techniques in the design, implementation and operational phases of a computational system.
Why is the course taught
Many phenomena observed in nature (such as evolution, self-organization and learning) can be understood as computational processes. Inspired in these phenomena, you will learn how to design algorithms and computers showing properties (such as adaptation, self-organization, energy efficiency) that are hard to achieve by means of conventional techniques developed in computer science and engineering.
- Kvasnička, V., Pospíchal J., Tiňo P.: Evolučné algoritmy. Vydavatelství STU Bratislava, 2000, 215 s., ISBN 80-227-1377-5
- Mařík et al.: Umělá inteligence IV, Academia, 2003, 480 s., ISBN 80-200-1044-0
- 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
- 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
- Miller J.F.: Cartesian Genetic Programming, Springer Verlag, 2011, ISBN 978-3-642-17309-7
- Sze V., Chen Y.H., Yang T.J., Emer J.S.: Efficient Processing of Deep Neural Networks. Morgan & Claypool Publishers, 2020, ISBN 978-1681738352
- Rozenberg G., Bäck T., Kok J.N.: Handbook of Natural Computing, Springer 2012, 2052 p., ISBN 978-3540929093
Syllabus of lectures
- Introduction, inspiration in biology, entropy and self-organization
- Limits of abstract and physical computing
- Evolutionary design
- Cartesian genetic programming
- Reconfigurable computing devices
- Evolutionary design of electronic circuits
- Evolvable hardware, applications
- Computational development
- Neural networks and neuroevolution
- Neural hardware
- DNA computing
- Nanotechnology and molecular electronics
- Recent trends
Syllabus of computer exercises
- Evolutionary design of combinational circuits
- Statistical evaluation of experiments with evolutionary design
- Celulární automaty
Syllabus - others, projects and individual work of students
Every student will choose one project from a list of approved projects that are relevant for this course. The implementation, presentation and documentation of the project will be evaluated.
Mid-term exam, project and its presentation, computer lab assignments.
Mid-term exam, realization and presentation of the project, computer lab assignments in due dates. The minimal number of points which can be obtained from the final exam is 20. Otherwise, no points will be assigned to a student. In the case of a reported barrier preventing the student to defend the project or solve a lab assignment, the student will be allowed to defend the project or solve the lab assignment on an alternative date.
Course inclusion in study plans
- Programme IT-MGR-2, field MBI, 1st year of study, Compulsory
- Programme IT-MGR-2, field MBS, MGM, MIS, MSK, any year of study, Elective
- Programme IT-MGR-2, field MIN, any year of study, Compulsory-Elective group I
- Programme IT-MGR-2, field MMM, any year of study, Compulsory-Elective group N
- Programme IT-MGR-2, field MPV, any year of study, Compulsory-Elective group B
- Programme MITAI, field NADE, NCPS, NEMB, NGRI, NHPC, NIDE, NISD, NISY, NISY up to 2020/21, NMAT, NNET, NSEC, NSEN, NSPE, NVER, NVIZ, any year of study, Elective
- Programme MITAI, field NBIO, NMAL, any year of study, Compulsory