An electromagnetic perspective of artificial intelligence neuromorphic chips
The emergence of artificial intelligence has represented great potential in solving a wide
range of complex problems. However, traditional general-purpose chips based on von …
range of complex problems. However, traditional general-purpose chips based on von …
OR Residual Connection Achieving Comparable Accuracy to ADD Residual Connection in Deep Residual Spiking Neural Networks
Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing
for their biological fidelity and the capacity to execute energy-efficient spike-driven …
for their biological fidelity and the capacity to execute energy-efficient spike-driven …
Recent progress on signal integrity modeling of neuromorphic chips by the PEEC method
With the rapid advances of artificial intelligence and its applications, the design of memristor-
based neuromorphic chips inspired by the human brain has become an important area of …
based neuromorphic chips inspired by the human brain has become an important area of …
Real-time execution of SNN models with synaptic plasticity for handwritten digit recognition on SIMD hardware
B Vallejo-Mancero, J Madrenas… - Frontiers in Neuroscience, 2024 - frontiersin.org
Recent advancements in neuromorphic computing have led to the development of hardware
architectures inspired by Spiking Neural Networks (SNNs) to emulate the efficiency and …
architectures inspired by Spiking Neural Networks (SNNs) to emulate the efficiency and …
Efficient sparse spiking auto-encoder for reconstruction, denoising and classification
B Walters, H Rahimian Kalatehbali, Z Cai… - Neuromorphic …, 2024 - iopscience.iop.org
Auto-encoders are capable of performing input reconstruction, denoising, and classification
through an encoder-decoder structure. Spiking Auto-Encoders (SAEs) can utilize …
through an encoder-decoder structure. Spiking Auto-Encoders (SAEs) can utilize …
S‐parameter extraction for electromagnetic modelling of memristor‐based crossbar array circuits
This article presents an efficient S‐parameter extraction method for memristor‐based
crossbar array circuits. The proposed approach involves converting the array into an …
crossbar array circuits. The proposed approach involves converting the array into an …
[HTML][HTML] Spiking neural network with local plasticity and sparse connectivity for audio classification
РБ Рыбка, ДС Власов, АИ Манжуров… - Известия высших …, 2024 - cyberleninka.ru
Purpose. Studying the possibility of implementing a data classification method based on a
spiking neural network, which has a low number of connections and is trained based on …
spiking neural network, which has a low number of connections and is trained based on …
IZVESTIYA VUZ. APPLIED NONLINEAR DYNAMICS
RB RYBKA, DS VLASOV, AI MANZHUROV… - … университет им. НГ …, 2024 - elibrary.ru
Purpose. Studying the possibility of implementing a data classification method based on a
spiking neural network, which has a low number of connections and is trained based on …
spiking neural network, which has a low number of connections and is trained based on …