Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Inadagbo, Kayode"'
Neuromorphic systems, inspired by the complexity and functionality of the human brain, have gained interest in academic and industrial attention due to their unparalleled potential across a wide range of applications. While their capabilities herald
Externí odkaz:
http://arxiv.org/abs/2401.12055
This paper presents a novel approach to neuromorphic audio processing by integrating the strengths of Spiking Neural Networks (SNNs), Transformers, and high-performance computing (HPC) into the HPCNeuroNet architecture. Utilizing the Intel N-DNS data
Externí odkaz:
http://arxiv.org/abs/2311.12449
This research delves into sophisticated neural network frameworks like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTMs), and Deep Belief Networks (DBNs) for improved analysis of ECG signals
Externí odkaz:
http://arxiv.org/abs/2311.12439
Autor:
Isik, Murat, Inadagbo, Kayode
This paper presents an innovative methodology for improving the robustness and computational efficiency of Spiking Neural Networks (SNNs), a critical component in neuromorphic computing. The proposed approach integrates astrocytes, a type of glial ce
Externí odkaz:
http://arxiv.org/abs/2309.08232
This study presents advanced neural network architectures including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTMs), and Deep Belief Networks (DBNs) for enhanced ECG signal analysis using
Externí odkaz:
http://arxiv.org/abs/2307.07914
Reconfigurable architectures like Field Programmable Gate Arrays (FPGAs) have been used for accelerating computations in several domains because of their unique combination of flexibility, performance, and power efficiency. However, FPGAs have not be
Externí odkaz:
http://arxiv.org/abs/2304.12474