Spiking neural networks and their applications: A review
The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …
domains. However, deep neural networks are very resource-intensive in terms of energy …
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 …
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 …
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 map** 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 …
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 …