Deep Directly-Trained Spiking Neural Networks for Object Detection
- URL: http://arxiv.org/abs/2307.11411v3
- Date: Thu, 27 Jul 2023 03:37:11 GMT
- Title: Deep Directly-Trained Spiking Neural Networks for Object Detection
- Authors: Qiaoyi Su and Yuhong Chou and Yifan Hu and Jianing Li and Shijie Mei
and Ziyang Zhang and Guoqi Li
- Abstract summary: EMS-YOLO is a novel directly-trained SNN framework for object detection.
We design a full-spike residual block, EMS-ResNet, which can effectively extend the depth of the directly-trained SNN with low power consumption.
It is shown that our model could achieve comparable performance to the ANN with the same architecture while consuming 5.83 times less energy.
- Score: 20.594942840081757
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Spiking neural networks (SNNs) are brain-inspired energy-efficient models
that encode information in spatiotemporal dynamics. Recently, deep SNNs trained
directly have shown great success in achieving high performance on
classification tasks with very few time steps. However, how to design a
directly-trained SNN for the regression task of object detection still remains
a challenging problem. To address this problem, we propose EMS-YOLO, a novel
directly-trained SNN framework for object detection, which is the first trial
to train a deep SNN with surrogate gradients for object detection rather than
ANN-SNN conversion strategies. Specifically, we design a full-spike residual
block, EMS-ResNet, which can effectively extend the depth of the
directly-trained SNN with low power consumption. Furthermore, we theoretically
analyze and prove the EMS-ResNet could avoid gradient vanishing or exploding.
The results demonstrate that our approach outperforms the state-of-the-art
ANN-SNN conversion methods (at least 500 time steps) in extremely fewer time
steps (only 4 time steps). It is shown that our model could achieve comparable
performance to the ANN with the same architecture while consuming 5.83 times
less energy on the frame-based COCO Dataset and the event-based Gen1 Dataset.
Related papers
- Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks [50.32980443749865]
Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biologicalability.
Current SNNs struggle to balance accuracy and latency in neuromorphic datasets.
We propose Step-wise Distillation (HSD) method, tailored for neuromorphic datasets.
arXiv Detail & Related papers (2024-09-19T06:52:34Z) - NAS-BNN: Neural Architecture Search for Binary Neural Networks [55.058512316210056]
We propose a novel neural architecture search scheme for binary neural networks, named NAS-BNN.
Our discovered binary model family outperforms previous BNNs for a wide range of operations (OPs) from 20M to 200M.
In addition, we validate the transferability of these searched BNNs on the object detection task, and our binary detectors with the searched BNNs achieve a novel state-of-the-art result, e.g., 31.6% mAP with 370M OPs, on MS dataset.
arXiv Detail & Related papers (2024-08-28T02:17:58Z) - Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking
Neural Networks with Learnable Neuronal Dynamics [6.309365332210523]
Spiking Neural Networks (SNNs) with their neuro-inspired event-driven processing can efficiently handle asynchronous data.
We propose an adaptive fully-spiking framework with learnable neuronal dynamics to alleviate the spike vanishing problem.
Our experiments on datasets show an average reduction of 13% in average endpoint error (AEE) compared to state-of-the-art ANNs.
arXiv Detail & Related papers (2022-09-21T21:17:56Z) - Training High-Performance Low-Latency Spiking Neural Networks by
Differentiation on Spike Representation [70.75043144299168]
Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware.
It is a challenge to efficiently train SNNs due to their non-differentiability.
We propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance.
arXiv Detail & Related papers (2022-05-01T12:44:49Z) - Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking
Neural Networks? [3.2108350580418166]
Spiking neural networks (SNNs) operate via binary spikes distributed over time.
SOTA training strategies for SNNs involve conversion from a non-spiking deep neural network (DNN)
We propose a new training algorithm that accurately captures these distributions, minimizing the error between the DNN and converted SNN.
arXiv Detail & Related papers (2021-12-22T18:47:45Z) - Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer [77.78479877473899]
We design a spatial-temporal-fusion BNN for efficiently scaling BNNs to large models.
Compared to vanilla BNNs, our approach can greatly reduce the training time and the number of parameters, which contributes to scale BNNs efficiently.
arXiv Detail & Related papers (2021-12-12T17:13:14Z) - 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) - Beyond Classification: Directly Training Spiking Neural Networks for
Semantic Segmentation [5.800785186389827]
Spiking Neural Networks (SNNs) have emerged as the low-power alternative to Artificial Neural Networks (ANNs)
In this paper, we explore the SNN applications beyond classification and present semantic segmentation networks configured with spiking neurons.
arXiv Detail & Related papers (2021-10-14T21:53:03Z) - Going Deeper With Directly-Trained Larger Spiking Neural Networks [20.40894876501739]
Spiking neural networks (SNNs) are promising in coding for bio-usible information and event-driven signal processing.
However, the unique working mode of SNNs makes them more difficult to train than traditional networks.
We propose a CIF-dependent batch normalization (tpladBN) method based on the emerging-temporal backproation threshold.
arXiv Detail & Related papers (2020-10-29T07:15:52Z) - 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) - SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object
Tracking [20.595208488431766]
SiamSNN is the first deep SNN tracker that achieves short latency and low precision loss on the visual object tracking benchmarks OTB2013, VOT2016, and GOT-10k.
SiamSNN notably achieves low energy consumption and real-time on Neuromorphic chip TrueNorth.
arXiv Detail & Related papers (2020-03-17T08:49:51Z)
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.