Advancements in algorithms and neuromorphic hardware for spiking neural networks
Artificial neural networks (ANNs) have experienced a rapid advancement for their success in
various application domains, including autonomous driving and drone vision. Researchers …
various application domains, including autonomous driving and drone vision. Researchers …
Integration and co-design of memristive devices and algorithms for artificial intelligence
Memristive devices share remarkable similarities to biological synapses, dendrites, and
neurons at both the physical mechanism level and unit functionality level, making the …
neurons at both the physical mechanism level and unit functionality level, making the …
A survey on neuromorphic computing: Models and hardware
The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …
on traditional computer systems. As the performance of traditional Von Neumann machines …
Encoding, model, and architecture: Systematic optimization for spiking neural network in FPGAs
Spiking neural network (SNN) has drawn research interests as it mimics dynamic activities of
human brain and has the potential to perform real-time cognitive tasks. However, latency …
human brain and has the potential to perform real-time cognitive tasks. However, latency …
Gisnet: Graph-based information sharing network for vehicle trajectory prediction
The trajectory prediction is a critical and challenging problem in the design of an
autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber …
autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber …
A low-cost, high-throughput neuromorphic computer for online SNN learning
Neuromorphic devices capable of training spiking neural networks (SNNs) are not easy to
develop due to two main factors: lack of efficient supervised learning algorithms, and high …
develop due to two main factors: lack of efficient supervised learning algorithms, and high …
A review of SNN implementation on FPGA
QT Pham, TQ Nguyen, PC Hoang… - … analysis and pattern …, 2021 - ieeexplore.ieee.org
Spiking Neural Network (SNN), the next generation of Neural Network, is supposed to be
more energy-saving than the previous generation represented by Convolution Neural …
more energy-saving than the previous generation represented by Convolution Neural …
Multivariate time series classification using spiking neural networks
There is an increasing demand to process streams of temporal data in energy-limited
scenarios such as embedded devices, driven by the advancement and expansion of Internet …
scenarios such as embedded devices, driven by the advancement and expansion of Internet …
A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models
S Sanaullah - Northern Lights Deep Learning Conference, 2024 - proceedings.mlr.press
In this article, we propose a novel standalone hybrid Spiking-Convolutional Neural Network
(SC-NN) model and test on using image inpainting tasks. Our approach uses the unique …
(SC-NN) model and test on using image inpainting tasks. Our approach uses the unique …
A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models
K Roy, U Rückert, T Jungeblut - arXiv preprint arXiv:2407.08861, 2024 - arxiv.org
In this article, we propose a novel standalone hybrid Spiking-Convolutional Neural Network
(SC-NN) model and test on using image inpainting tasks. Our approach uses the unique …
(SC-NN) model and test on using image inpainting tasks. Our approach uses the unique …