A Hybrid SNN-ANN Network for Event-based Object Detection with Spatial and Temporal Attention

• URL: http://arxiv.org/abs/2403.10173v1
• Date: Fri, 15 Mar 2024 10:28:31 GMT
• Title: A Hybrid SNN-ANN Network for Event-based Object Detection with Spatial and Temporal Attention
• Authors: Soikat Hasan Ahmed, Jan Finkbeiner, Emre Neftci,
• Abstract summary: Event cameras offer high temporal resolution and dynamic range with minimal motion blur, making them promising for object detection tasks. While Spiking Neural Networks (SNNs) are a natural match for event-based sensory data, Artificial Neural Networks (ANNs) tend to display more stable training dynamics. We introduce the first Hybrid Attention-based SNN-ANN backbone for object detection using event cameras.
• Score: 2.5075774828443467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event cameras offer high temporal resolution and dynamic range with minimal motion blur, making them promising for object detection tasks. While Spiking Neural Networks (SNNs) are a natural match for event-based sensory data and enable ultra-energy efficient and low latency inference on neuromorphic hardware, Artificial Neural Networks (ANNs) tend to display more stable training dynamics and faster convergence resulting in greater task performance. Hybrid SNN-ANN approaches are a promising alternative, enabling to leverage the strengths of both SNN and ANN architectures. In this work, we introduce the first Hybrid Attention-based SNN-ANN backbone for object detection using event cameras. We propose a novel Attention-based SNN-ANN bridge module to capture sparse spatial and temporal relations from the SNN layer and convert them into dense feature maps for the ANN part of the backbone. Experimental results demonstrate that our proposed method surpasses baseline hybrid and SNN-based approaches by significant margins, with results comparable to existing ANN-based methods. Extensive ablation studies confirm the effectiveness of our proposed modules and architectural choices. These results pave the way toward a hybrid SNN-ANN architecture that achieves ANN like performance at a drastically reduced parameter budget. We implemented the SNN blocks on digital neuromorphic hardware to investigate latency and power consumption and demonstrate the feasibility of our approach.

Related papers

• Towards Efficient Deployment of Hybrid SNNs on Neuromorphic and Edge AI Hardware [0.493599216374976]
  This paper explores the synergistic potential of neuromorphic and edge computing to create a versatile machine learning (ML) system tailored for processing data captured by dynamic vision sensors. We construct and train hybrid models, blending spiking neural networks (SNNs) and artificial neural networks (ANNs) using PyTorch and Lava frameworks.
  arXiv  Detail & Related papers  (2024-07-11T17:40:39Z)
• LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding [55.64533786293656]
  We show that our algorithm can achieve a near-perfect mapping between the activation values of an ANN and the spike times of an SNN on a number of challenging AI tasks. The study paves the way for deploying ultra-low-power TTFS-based SNNs on power-constrained edge computing platforms.
  arXiv  Detail & Related papers  (2023-10-23T14:26:16Z)
• Highly Efficient SNNs for High-speed Object Detection [7.3074002563489024]
  Experimental results show that our efficient SNN can achieve 118X speedup on GPU with only 1.5MB parameters for object detection tasks. We further verify our SNN on FPGA platform and the proposed model can achieve 800+FPS object detection with extremely low latency.
  arXiv  Detail & Related papers  (2023-09-27T10:31:12Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• Spikeformer: A Novel Architecture for Training High-Performance Low-Latency Spiking Neural Network [6.8125324121155275]
  We propose a novel Transformer-based SNN,termed "Spikeformer",which outperforms its ANN counterpart on both static dataset and neuromorphic dataset. Remarkably,our Spikeformer outperforms other SNNs on ImageNet by a large margin (i.e.more than 5%) and even outperforms its ANN counterpart by 3.1% and 2.2% on DVS-Gesture and ImageNet.
  arXiv  Detail & Related papers  (2022-11-19T12:49:22Z)
• SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks [117.56823277328803]
  Spiking neural networks are efficient computation models for low-power environments. We propose a SNN-to-ANN (SNN2ANN) framework to train the SNN in a fast and memory-efficient way. Experiment results show that our SNN2ANN-based models perform well on the benchmark datasets.
  arXiv  Detail & Related papers  (2022-06-19T16:52:56Z)
• 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)
• 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)
• 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.