Exploring scaling efficiency of intel loihi neuromorphic processor

Name: Exploring scaling efficiency of intel loihi neuromorphic processor
Author: Uludağ, Recep Buğra, Çaǧdaş, S., Işler, Y. S., Şengör, N. S., Aktürk, İsmail

İsim	Exploring scaling efficiency of intel loihi neuromorphic processor
Yazar	Uludağ, Recep Buğra, Çaǧdaş, S., Işler, Y. S., Şengör, N. S., Aktürk, İsmail
Basım Tarihi:	2023
Basım Yeri	- IEEE
Konu	Intel loihi, Scaling efficiency, Spiking neural networks, Winner-take-all
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-835032649-9
Kayıt Numarası	8ef7ac0e-dce0-4627-b51f-5989a8632432
Lokasyon	Computer Science
Tarih	2023
Notlar	Intel’s Neuromorphic Research Community
Örnek Metin	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

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Exploring scaling efficiency of intel loihi neuromorphic processor

Yazar Uludağ, Recep Buğra, Çaǧdaş, S., Işler, Y. S., Şengör, N. S., Aktürk, İsmail

Basım Tarihi 2023

Basım Yeri - IEEE

Konu Intel loihi, Scaling efficiency, Spiking neural networks, Winner-take-all

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-835032649-9

Kayıt Numarası 8ef7ac0e-dce0-4627-b51f-5989a8632432

Lokasyon Computer Science

Tarih 2023

Notlar Intel’s Neuromorphic Research Community

Örnek Metin 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