Eintrag in der Universitätsbibliographie der TU Chemnitz
Volltext zugänglich unter
URN: urn:nbn:de:bsz:ch1-qucosa2-980221
Dinkelbach, Helge Ülo
Hamker, Fred H. (Prof. Dr.) ; Rougier, Nicolas P. (Dr. habil.) (Gutachter)
Efficient Simulation of Biologically Realistic Neural Networks on Different Parallel Hardware Using Code Generation
Kurzfassung in englisch
Computational neuroscience is a rapidly developing field exploring the principles of information encoding and decoding in neural systems and trying to understand the brain on a functional level. The ongoing research in this field leads to models increasing in size and complexity. Modern multi-core CPUs and graphic processing units (GPUs) offer increasing computational power on shared memory systems. Neural simulation tools should help to make use of this parallel computational power for the simulation of biologically inspired networks. As we will show in this thesis, developing such neural simulation tools demands a good understanding of both models of biologically inspired networks and current hardware architectures. The simulation of rate-coded and spiking models places different requirements on their efficient implementation. At this point, one rapidly notices the problem of specialization and generalization in simulation frameworks. Code generation approaches, already used in Brian, GeNN, or ANNarchy, seem to be a suitable solution for this dilemma. Code generation allows the adjustment of generated simulation code based on the used hardware platform and the structure of the target network. In this thesis, we will discuss the implementation of key operations within rate-coded and spiking neural networks and the impact of different data representations on those. Based on this acquired knowledge, we selected the code templates used in the code generation of our neural simulation framework ANNarchy. In summary, we could achieve a noticeable improvement on rate-coded neural models while we achieve comparable performance on spiking model benchmarks on shared memory systems.
| Universität: | Technische Universität Chemnitz | |
| Institut: | Professur Künstliche Intelligenz | |
| Fakultät: | Fakultät für Informatik | |
| Dokumentart: | Dissertation | |
| Betreuer: | Hamker, Fred H. (Prof. Dr.) | |
| DOI: | doi:10.60687/2025-0144 | |
| SWD-Schlagwörter: | Neuronales Netz , Programmierung , Codegenerierung | |
| Freie Schlagwörter (Englisch): | Neural Simulator , ANNarchy , Code Generation | |
| DDC-Sachgruppe: | Datenverarbeitung; Informatik, Computerprogrammierung, Programme, Daten | |
| Sprache: | englisch | |
| Tag der mündlichen Prüfung | 02.10.2024 |
