Author
Uludağ, Recep Buğra, Çaǧdaş, S., Işler, Y. S., Şengör, N. S., Aktürk, İsmail
Publication Date
2023
Publication Place
-
IEEE
Subject
Intel loihi, Scaling efficiency, Spiking neural networks, Winner-take-all
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
979-835032649-9
Record ID
8ef7ac0e-dce0-4627-b51f-5989a8632432
Library Location
Computer Science
Date
2023
Notes
Intel’s Neuromorphic Research Community
Sample Text
In 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.
DOI
10.1109/ICECS58634.2023.10382884
