Low power stochastic computing spiking neural networks - Osama Ahmed Rashad Abdelhameed; Amin Mohamed Nassar; Hassan Mostafa Hassan (2024)
This thesis presents an innovative low power approach to design hardware for Spiking Neural Networks (SNNs), a cutting-edge type of deep learning model that closely emulates the way the human brain processes information. The thesis focuses on implementing SNNs with a simple yet accurate neuron model: Izhikevich model using stochastic computing, a technique that replaces precise numerical calculations with probabilistic approximations. The thesis introduces replacing the conventional power and area hungry multiplier with a stochastic one which leads to significant reduction in area and power consumption
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Thesis (M.Sc)-Cairo University, 2024.
Bibliography: pages 84-87.
Issues also as CD.
Text in English and abstract in Arabic & English.
Keywords: Communication networks; (3THcGJ GdGJUGdGJ s; Neural Networks -- Spiking Neural Networks -- Deep learning -- Artificial Intelligence -- Stochastic Computing -- Low power circuits