Deep residual learning in spiking neural networks
Abstract Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-
based approaches due to discrete binary activation and complex spatial-temporal dynamics …
based approaches due to discrete binary activation and complex spatial-temporal dynamics …
Temporal efficient training of spiking neural network via gradient re-weighting
Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread
research interest because of their event-driven and energy-efficient characteristics. Still, it is …
research interest because of their event-driven and energy-efficient characteristics. Still, it is …
N-imagenet: Towards robust, fine-grained object recognition with event cameras
We introduce N-ImageNet, a large-scale dataset targeted for robust, fine-grained object
recognition with event cameras. The dataset is collected using programmable hardware in …
recognition with event cameras. The dataset is collected using programmable hardware in …
S2n2: A fpga accelerator for streaming spiking neural networks
Spiking Neural Networks (SNNs) are the next generation of Artificial Neural Networks
(ANNs) that utilize an event-based representation to perform more efficient computation …
(ANNs) that utilize an event-based representation to perform more efficient computation …
[HTML][HTML] A novel out-of-distribution detection approach for spiking neural networks: design, fusion, performance evaluation and explainability
Abstract Research around Spiking Neural Networks has ignited during the last years due to
their advantages when compared to traditional neural networks, including their efficient …
their advantages when compared to traditional neural networks, including their efficient …
Ev-tta: Test-time adaptation for event-based object recognition
We introduce Ev-TTA, a simple, effective test-time adaptation algorithm for event-based
object recognition. While event cameras are proposed to provide measurements of scenes …
object recognition. While event cameras are proposed to provide measurements of scenes …
A survey of spiking neural network accelerator on FPGA
M Isik - arXiv preprint arXiv:2307.03910, 2023 - arxiv.org
Due to the ability to implement customized topology, FPGA is increasingly used to deploy
SNNs in both embedded and high-performance applications. In this paper, we survey state …
SNNs in both embedded and high-performance applications. In this paper, we survey state …
Hardvs: Revisiting human activity recognition with dynamic vision sensors
The main streams of human activity recognition (HAR) algorithms are developed based on
RGB cameras which usually suffer from illumination, fast motion, privacy preservation, and …
RGB cameras which usually suffer from illumination, fast motion, privacy preservation, and …
SSTFormer: bridging spiking neural network and memory support transformer for frame-event based recognition
Event camera-based pattern recognition is a newly arising research topic in recent years.
Current researchers usually transform the event streams into images, graphs, or voxels, and …
Current researchers usually transform the event streams into images, graphs, or voxels, and …
PCA dimensionality reduction method for image classification
B Zhao, X Dong, Y Guo, X Jia, Y Huang - Neural Processing Letters, 2022 - Springer
The pooling layer has achieved good results in reducing the feature dimension and
parameters of convolution neural network (CNN), but it will cause different degrees of …
parameters of convolution neural network (CNN), but it will cause different degrees of …