Solving xor in spike neural network (snn) with component-off-the-shelf
Yazarlar (5)
Sevde Vuslat Çıkıkcı
Eren Örek
Ali Kağan Özgen
Arda Yavuz
Dr. Öğr. Üyesi Tuba AYHAN Sinop Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| Kongre Adı | 2024 15th National Conference on Electrical and Electronics Engineering (ELECO) | ||
| Kongre Tarihi | / | ||
| Basıldığı Ülke | Basıldığı Şehir | ||
| Bildiri Linki | https://ieeexplore.ieee.org/abstract/document/10847089/ | ||
| UAK Araştırma Alanları |
Halı-Kilim ve Dokuma
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| Özet |
| This paper addresses the solution of the XOR problem with Spiking Neural Networks (SNN) in order to improve energy efficiency and computational performance as Moore's Law approaches its limits. SNN is capable of solving nonlinear problems while saving energy by mimicking the working principles of biological neurons. For this purpose, a SNN consisting of 12 neurons was implemented on a breadboard using the Leaky Integrate and Fire (LIF) model. In the input layer of the network, 50 Hz and 100 Hz signals are processed with frequency sensitive filters. With the help of bandpass and low-pass filters, additive and inverting operational amplifiers, the XOR problem is successfully solved. |
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