Abstract
Neuromorphic computing has emerged as a promising solution to the power and memory requirements of embedded systems. However, mapping pre-trained neural networks onto diverse neuromorphic hardware architectures presents significant challenges. In this paper, we introduce GMap, a versatile, easy-to-use, and open-source python library designed for mapping neural networks onto any arbitrary hardware architecture. GMap adapts the simulated annealing approach, offering a probabilistic, meta-heuristic optimization for approximating the global optimum mapping. The algorithm takes architectural parameters of the hardware and network connectivity as input and provides a mapping solution as output. The library allows users to either map a pre-trained network onto a pre-existing chip or define custom hardware with respect to its constraints. GMap contributes to the neuromorphic field by enabling efficient deployment of neural networks on various hardware platforms and facilitating further research and collaboration in the neuromorphic community.
Weber, Jimmy