Brain-inspired event-based neuromorphic processing systems have emerged as a promising technology in particular for bio-medical circuits and systems. However, both neuromorphic and biological implementations of neural networks have critical energy and memory constraints. To minimize the use of memory resources in multi-core neuromorphic processors, we propose a network design approach inspired by biological neural networks. We use this approach to design a new routing scheme optimized for small-world networks and, at the same time, to present a hardware-aware placement algorithm that optimizes the allocation of resources for small-world network models. We validate the algorithm with a canonical small-world network and present preliminary results for other networks derived from it
@article{arxiv.2208.13587,
title = {Cortical-inspired placement and routing: minimizing the memory resources in multi-core neuromorphic processors},
author = {Vanessa R. C. Leite and Zhe Su and Adrian M. Whatley and Giacomo Indiveri},
journal= {arXiv preprint arXiv:2208.13587},
year = {2022}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2203.00655