Implementing spiking neural networks on neuromorphic architectures: A review
Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …
systems to execute machine learning applications that are designed using Spiking Neural …
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 …
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 …
A design flow for mapping 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) …
Automated generation of integrated digital and spiking neuromorphic machine learning accelerators
The growing numbers of application areas for artificial intelligence (AI) methods have led to
an explosion in availability of domain-specific accelerators, which struggle to support every …
an explosion in availability of domain-specific accelerators, which struggle to support every …
A design flow for scheduling spiking deep convolutional neural networks on heterogeneous neuromorphic system-on-chip
A Das - ACM Transactions on Embedded Computing Systems, 2023 - dl.acm.org
Neuromorphic systems-on-chip (NSoCs) integrate CPU cores and neuromorphic hardware
accelerators on the same chip. These platforms can execute spiking deep convolutional …
accelerators on the same chip. These platforms can execute spiking deep convolutional …
Energy-efficient respiratory anomaly detection in premature newborn infants
Precise monitoring of respiratory rate in premature newborn infants is essential to initiating
medical interventions as required. Wired technologies can be invasive and obtrusive to the …
medical interventions as required. Wired technologies can be invasive and obtrusive to the …
Design-technology co-optimization for NVM-based neuromorphic processing elements
An emerging use case of machine learning (ML) is to train a model on a high-performance
system and deploy the trained model on energy-constrained embedded systems …
system and deploy the trained model on energy-constrained embedded systems …
On the mitigation of read disturbances in neuromorphic inference hardware
Non-Volatile Memory (NVM) cells are used in neuromorphic hardware to store model
parameters, which are programmed as resistance states. NVMs suffer from the read disturb …
parameters, which are programmed as resistance states. NVMs suffer from the read disturb …