An efficient spiking neural network for recognizing gestures with a dvs camera on the loihi neuromorphic processor
R Massa, A Marchisio, M Martina… - 2020 International Joint …, 2020 - ieeexplore.ieee.org
Spiking Neural Networks (SNNs), the third generation NNs, have come under the spotlight
for machine learning based applications due to their biological plausibility and reduced …
for machine learning based applications due to their biological plausibility and reduced …
Carsnn: An efficient spiking neural network for event-based autonomous cars on the loihi neuromorphic research processor
A Viale, A Marchisio, M Martina… - … Joint Conference on …, 2021 - ieeexplore.ieee.org
Autonomous Driving (AD) related features provide new forms of mobility that are also
beneficial for other kind of intelligent and autonomous systems like robots, smart …
beneficial for other kind of intelligent and autonomous systems like robots, smart …
An event-driven classifier for spiking neural networks fed with synthetic or dynamic vision sensor data
E Stromatias, M Soto, T Serrano-Gotarredona… - Frontiers in …, 2017 - frontiersin.org
This paper introduces a novel methodology for training an event-driven classifier within a
Spiking Neural Network (SNN) System capable of yielding good classification results when …
Spiking Neural Network (SNN) System capable of yielding good classification results when …
Deep spiking neural networks with binary weights for object recognition
Spiking neural networks (SNNs) have shown great potential as a solution for realizing
ultralow-power consumption on neuromorphic hardware, but obtaining deep SNNs is still a …
ultralow-power consumption on neuromorphic hardware, but obtaining deep SNNs is still a …
A review of algorithms and hardware implementations for spiking neural networks
Deep Learning (DL) has contributed to the success of many applications in recent years.
The applications range from simple ones such as recognizing tiny images or simple speech …
The applications range from simple ones such as recognizing tiny images or simple speech …
Deep spiking neural network with spike count based learning rule
Deep spiking neural networks (SNNs) support asynchronous event-driven computation,
massive parallelism and demonstrate great potential to improve the energy efficiency of its …
massive parallelism and demonstrate great potential to improve the energy efficiency of its …
[PDF][PDF] LISNN: Improving spiking neural networks with lateral interactions for robust object recognition.
Abstract Spiking Neural Network (SNN) is considered more biologically plausible and
energy-efficient on emerging neuromorphic hardware. Recently backpropagation algorithm …
energy-efficient on emerging neuromorphic hardware. Recently backpropagation algorithm …
Real spike: Learning real-valued spikes for spiking neural networks
Y Guo, L Zhang, Y Chen, X Tong, X Liu… - … on Computer Vision, 2022 - Springer
Brain-inspired spiking neural networks (SNNs) have recently drawn more and more
attention due to their event-driven and energy-efficient characteristics. The integration of …
attention due to their event-driven and energy-efficient characteristics. The integration of …
Rethinking the performance comparison between SNNS and ANNS
Artificial neural networks (ANNs), a popular path towards artificial intelligence, have
experienced remarkable success via mature models, various benchmarks, open-source …
experienced remarkable success via mature models, various benchmarks, open-source …
Going deeper in spiking neural networks: VGG and residual architectures
Over the past few years, Spiking Neural Networks (SNNs) have become popular as a
possible pathway to enable low-power event-driven neuromorphic hardware. However, their …
possible pathway to enable low-power event-driven neuromorphic hardware. However, their …