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[HTML][HTML] Memristor-based spiking neural networks: cooperative development of neural network architecture/algorithms and memristors
H Peng, L Gan, X Guo - Chip, 2024 - Elsevier
Inspired by the structure and principles of the human brain, spike neural networks (SNNs)
appear as the latest generation of artificial neural networks, attracting significant and …
appear as the latest generation of artificial neural networks, attracting significant and …
Implementing spiking neural networks on neuromorphic architectures: A review
PK Huynh, ML Varshika, A Paul, M Isik, A Balaji… - ar** of spiking neural networks to neuromorphic hardware
Neuromorphic computing systems are embracing memristors to implement high density and
low power synaptic storage as crossbar arrays in hardware. These systems are energy …
low power synaptic storage as crossbar arrays in hardware. These systems are energy …
Toward the optimal design and FPGA implementation of spiking neural networks
The performance of a biologically plausible spiking neural network (SNN) largely depends
on the model parameters and neural dynamics. This article proposes a parameter …
on the model parameters and neural dynamics. This article proposes a parameter …
A hybrid spiking neural network reinforcement learning agent for energy-efficient object manipulation
Due to the wide spread of robotics technologies in everyday activities, from industrial
automation to domestic assisted living applications, cutting-edge techniques such as deep …
automation to domestic assisted living applications, cutting-edge techniques such as deep …
Compiling spiking neural networks to neuromorphic hardware
Machine learning applications that are implemented with spike-based computation model,
eg, Spiking Neural Network (SNN), have a great potential to lower the energy consumption …
eg, Spiking Neural Network (SNN), have a great potential to lower the energy consumption …
Design of many-core big little µBrains for energy-efficient embedded neuromorphic computing
As spiking-based deep learning inference applications are increasing in embedded
systems, these systems tend to integrate neuromorphic accelerators such as µBrain to …
systems, these systems tend to integrate neuromorphic accelerators such as µBrain to …
DFSynthesizer: Dataflow-based synthesis of spiking neural networks to neuromorphic hardware
Spiking Neural Networks (SNNs) are an emerging computation model that uses event-
driven activation and bio-inspired learning algorithms. SNN-based machine learning …
driven activation and bio-inspired learning algorithms. SNN-based machine learning …