EvSegSNN: Neuromorphic Semantic Segmentation for Event Data
- URL: http://arxiv.org/abs/2406.14178v1
- Date: Thu, 20 Jun 2024 10:36:24 GMT
- Title: EvSegSNN: Neuromorphic Semantic Segmentation for Event Data
- Authors: Dalia Hareb, Jean Martinet,
- Abstract summary: EvSegSNN is a biologically plausible encoder-decoder U-shaped architecture relying on Parametric Leaky Integrate and Fire neurons.
We introduce an end-to-end biologically inspired semantic segmentation approach by combining Spiking Neural Networks with event cameras.
Experiments conducted on DDD17 demonstrate that EvSegSNN outperforms the closest state-of-the-art model in terms of MIoU.
- Score: 0.6138671548064356
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Semantic segmentation is an important computer vision task, particularly for scene understanding and navigation of autonomous vehicles and UAVs. Several variations of deep neural network architectures have been designed to tackle this task. However, due to their huge computational costs and their high memory consumption, these models are not meant to be deployed on resource-constrained systems. To address this limitation, we introduce an end-to-end biologically inspired semantic segmentation approach by combining Spiking Neural Networks (SNNs, a low-power alternative to classical neural networks) with event cameras whose output data can directly feed these neural network inputs. We have designed EvSegSNN, a biologically plausible encoder-decoder U-shaped architecture relying on Parametric Leaky Integrate and Fire neurons in an objective to trade-off resource usage against performance. The experiments conducted on DDD17 demonstrate that EvSegSNN outperforms the closest state-of-the-art model in terms of MIoU while reducing the number of parameters by a factor of $1.6$ and sparing a batch normalization stage.
Related papers
- Exploring Neural Network Pruning with Screening Methods [3.443622476405787]
Modern deep learning models have tens of millions of parameters which makes the inference processes resource-intensive.
This paper proposes and evaluates a network pruning framework that eliminates non-essential parameters.
The proposed framework produces competitive lean networks compared to the original networks.
arXiv Detail & Related papers (2025-02-11T02:31:04Z) - Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks.
embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption.
split computing - where an SNN is partitioned across two devices - is a promising solution.
This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
arXiv Detail & Related papers (2024-11-07T14:08:35Z) - MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network [8.53512216864715]
Spiking neural network is a novel event-based computational paradigm that is considered to be well suited for processing event camera tasks.
This work proposes a spiking neural network architecture with a novel residual block designed and multi-dimension attention modules combined.
This model outperforms previous ANN networks of the same size on the MVSEC dataset and shows great computational efficiency.
arXiv Detail & Related papers (2022-11-22T10:35:36Z) - Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency.
We present an IPU-optimized release of our custom SNN Python package, snnTorch.
arXiv Detail & Related papers (2022-11-19T15:44:08Z) - Object Detection with Spiking Neural Networks on Automotive Event Data [0.0]
We propose to train spiking neural networks (SNNs) directly on data coming from event cameras to design fast and efficient automotive embedded applications.
In this paper, we conducted experiments on two automotive event datasets, establishing new state-of-the-art classification results for spiking neural networks.
arXiv Detail & Related papers (2022-05-09T14:39:47Z) - Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for
Event-Based Vision [64.71260357476602]
Event-based vision sensors encode local pixel-wise brightness changes in streams of events rather than image frames.
Recent progress in object recognition from event-based sensors has come from conversions of deep neural networks.
We propose a hybrid architecture for end-to-end training of deep neural networks for event-based pattern recognition and object detection.
arXiv Detail & Related papers (2021-12-06T23:45:58Z) - CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded
Systems [0.0]
A Convolutional Neural Network (CNN) is a class of Deep Neural Network (DNN) widely used in the analysis of visual images captured by an image sensor.
In this paper, we propose a neoteric variant of deep convolutional neural network architecture to ameliorate the performance of existing CNN architectures for real-time inference on embedded systems.
arXiv Detail & Related papers (2021-12-01T18:20:52Z) - SpikeMS: Deep Spiking Neural Network for Motion Segmentation [7.491944503744111]
textitSpikeMS is the first deep encoder-decoder SNN architecture for the real-world large-scale problem of motion segmentation.
We show that textitSpikeMS is capable of textitincremental predictions, or predictions from smaller amounts of test data than it is trained on.
arXiv Detail & Related papers (2021-05-13T21:34:55Z) - Binary Graph Neural Networks [69.51765073772226]
Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data.
In this paper, we present and evaluate different strategies for the binarization of graph neural networks.
We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks.
arXiv Detail & Related papers (2020-12-31T18:48:58Z) - Modeling from Features: a Mean-field Framework for Over-parameterized
Deep Neural Networks [54.27962244835622]
This paper proposes a new mean-field framework for over- parameterized deep neural networks (DNNs)
In this framework, a DNN is represented by probability measures and functions over its features in the continuous limit.
We illustrate the framework via the standard DNN and the Residual Network (Res-Net) architectures.
arXiv Detail & Related papers (2020-07-03T01:37:16Z) - Progressive Tandem Learning for Pattern Recognition with Deep Spiking
Neural Networks [80.15411508088522]
Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency.
We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
arXiv Detail & Related papers (2020-07-02T15:38:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.