Uludağ, Recep BuğraÇaǧdaş, S.Işler, Y. S.Şengör, N. S.Aktürk, İsmail2024-02-272024-02-272023979-835032649-9http://hdl.handle.net/10679/9233https://doi.org/10.1109/ICECS58634.2023.10382884In this paper, we focus on examining how scaling efficiency evolves in winner-take-all (WTA) network models on Intel Loihi neuromorphic processor, as network-related features such as network size, neuron type, and connectivity scheme change. By analyzing these relationships, our study aims to shed light on the intricate interplay between SNN features and the efficiency of neuromorphic systems as they scale up. The findings presented in this paper are expected to enhance the comprehension of scaling efficiency in neuromorphic hardware, providing valuable insights for researchers and developers in optimizing the performance of large-scale SNNs on neuromorphic architectures.enginfo:eu-repo/semantics/restrictedAccessConference paper10.1109/ICECS58634.2023.10382884Intel loihiScaling efficiencySpiking neural networksWinner-take-all2-s2.0-85183587411