Year: 2024
Type: conference-paper
Abstract: 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.
Authors: Sevde Vuslat Çıkıkcı, Eren Örek, Ali Kağan Özgen, Arda Yavuz, Tuba Ayhan
Institution: MEF University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4406728888