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.