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Testability and dependability of AI hardware: Survey, trends, challenges, and perspectives
Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …
underlying technologies than those used in traditional digital processors or logic circuits …
Implementing spiking neural networks on neuromorphic architectures: A review
PK Huynh, ML Varshika, A Paul, M Isik, A Balaji… - ar** spiking neural networks to many-core neuromorphic hardware
The design of many-core neuromorphic hardware is becoming increasingly complex as
these systems are now expected to execute large machine-learning models. A predictable …
these systems are now expected to execute large machine-learning models. A predictable …
NeuroXplorer 1.0: An extensible framework for architectural exploration with spiking neural networks
Recently, both industry and academia have proposed many different neuromorphic
architectures to execute applications that are designed with Spiking Neural Network (SNN) …
architectures to execute applications that are designed with Spiking Neural Network (SNN) …
On the role of system software in energy management of neuromorphic computing
Neuromorphic computing systems such as DYNAPs and Loihi have recently been
introduced to the computing community to improve performance and energy efficiency of …
introduced to the computing community to improve performance and energy efficiency of …
[HTML][HTML] Nonvolatile memories in spiking neural network architectures: Current and emerging trends
A sustainable computing scenario demands more energy-efficient processors.
Neuromorphic systems mimic biological functions by employing spiking neural networks for …
Neuromorphic systems mimic biological functions by employing spiking neural networks for …
Fault-tolerant spiking neural network map** algorithm and architecture to 3D-NoC-based neuromorphic systems
Neuromorphic computing uses spiking neuron network models to solve machine learning
problems in a more energy-efficient way when compared to conventional artificial neural …
problems in a more energy-efficient way when compared to conventional artificial neural …