The past decades have seen an exponential growth in digital electronic computing, captured by Moore's Law. This has arguably left a blind spot on alternative approaches to data processing.

In this experimental course we survey, and look into, some of these “unconventional” computing paradigms.

Teacher: Martin Ziegler (use only this email address!)

Location: online

Schedule: Thursdays+Fridays, 14h30 to 16h00 KST

Language: English only (except for students discussing in KLMS)

Prerequisites: CS204 Discrete Mathematics and CS300 Introduction to Algorithms

Preferred: additional background in (one of) Physics OR Chemistry OR Biology or CS322 or CS422

Grading: S/U, students must get assigned and, after the Midterm, present (40~60min) one topic from the textbook 'Unconventional Computing'

Absences: 4 “spontaneous” absences, plus any reasonable pre-excused absences

0. Introduction (PPT, PDF)
1. Conventional Computing (PPT, PDF)
2. Asymptotic Computing
3. Analog Computing
4. Quantum Computing
5. Cellular Automata
6. Swarm Computing
MIDTERM
10.~20: Student presentations:

• Artificial Chemistry + Reaction-Diffusion + Membrane + P Computing (4x)
• DNA+Molecular+Bacterial+Cellular Computing (4x)
• Slime Mold Computing
• Reservoir Computing
• Amorphous Computing
• Social Algorithms
• Inductive Turing Machines (requires CS422!)
• Unconventional Problems

• https://en.wikipedia.org/wiki/Unconventional_computing
• https://sites.google.com/view/ucnc-2024/conference/previous-conferences
• https://doi.org/10.1038/s41598-020-68834-1
• http://doi.org/10.1007/978-1-4939-6883-1