SkipSNN: Efficiently Classifying Spike Trains with Event-attention

• URL: http://arxiv.org/abs/2411.05806v1
• Date: Tue, 29 Oct 2024 03:19:25 GMT
• Title: SkipSNN: Efficiently Classifying Spike Trains with Event-attention
• Authors: Hang Yin, Yao Su, Liping Liu, Thomas Hartvigsen, Xin Dai, Xiangnan Kong,
• Abstract summary: Spike train classification has recently become an important topic in the machine learning community. A promising model for it should follow the design principle of performing intensive computation only when signals of interest appear. This paper introduces an event-attention mechanism that enables SNNs to dynamically highlight useful signals of the original spike trains.
• Score: 29.639889737632842
• License:
• Abstract: Spike train classification has recently become an important topic in the machine learning community, where each spike train is a binary event sequence with \emph{temporal-sparsity of signals of interest} and \emph{temporal-noise} properties. A promising model for it should follow the design principle of performing intensive computation only when signals of interest appear. So such tasks use mainly Spiking Neural Networks (SNNs) due to their consideration of temporal-sparsity of spike trains. However, the basic mechanism of SNNs ignore the temporal-noise issue, which makes them computationally expensive and thus high power consumption for analyzing spike trains on resource-constrained platforms. As an event-driven model, an SNN neuron makes a reaction given any input signals, making it difficult to quickly find signals of interest. In this paper, we introduce an event-attention mechanism that enables SNNs to dynamically highlight useful signals of the original spike trains. To this end, we propose SkipSNN, which extends existing SNN models by learning to mask out noise by skipping membrane potential updates and shortening the effective size of the computational graph. This process is analogous to how people choose to open and close their eyes to filter the information they see. We evaluate SkipSNN on various neuromorphic tasks and demonstrate that it achieves significantly better computational efficiency and classification accuracy than other state-of-the-art SNNs.

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)
• Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning [91.29876772547348]
  Spiking neural networks (SNNs) are investigated as biologically inspired models of neural computation. This paper reveals that SNNs, when amalgamated with synaptic delay and temporal coding, are proficient in executing (knowledge) graph reasoning.
  arXiv  Detail & Related papers  (2024-05-27T05:53:30Z)
• High-performance deep spiking neural networks with 0.3 spikes per neuron [9.01407445068455]
  It is hard to train biologically-inspired spiking neural networks (SNNs) than artificial neural networks (ANNs) We show that training deep SNN models achieves the exact same performance as that of ANNs. Our SNN accomplishes high-performance classification with less than 0.3 spikes per neuron, lending itself for an energy-efficient implementation.
  arXiv  Detail & Related papers  (2023-06-14T21:01:35Z)
• 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)
• A Spiking Neural Network Structure Implementing Reinforcement Learning [0.0]
  In the present paper, I describe an SNN structure which, seemingly, can be used in wide range of reinforcement learning tasks. The SNN structure considered in the paper includes spiking neurons described by a generalization of the LIFAT (leaky integrate-and-fire neuron with adaptive threshold) model. My concept is based on very general assumptions about RL task characteristics and has no visible limitations on its applicability.
  arXiv  Detail & Related papers  (2022-04-09T09:08:10Z)
• A Time Encoding approach to training Spiking Neural Networks [3.655021726150368]
  Spiking Neural Networks (SNNs) have been gaining in popularity. In this paper, we provide an extra tool to help us understand and train SNNs by using theory from the field of time encoding.
  arXiv  Detail & Related papers  (2021-10-13T14:07:11Z)
• 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)
• Tensor train decompositions on recurrent networks [60.334946204107446]
  Matrix product state (MPS) tensor trains have more attractive features than MPOs, in terms of storage reduction and computing time at inference. We show that MPS tensor trains should be at the forefront of LSTM network compression through a theoretical analysis and practical experiments on NLP task.
  arXiv  Detail & Related papers  (2020-06-09T18:25:39Z)
• Event-Based Angular Velocity Regression with Spiking Networks [51.145071093099396]
  Spiking Neural Networks (SNNs) process information conveyed as temporal spikes rather than numeric values. We propose, for the first time, a temporal regression problem of numerical values given events from an event camera. We show that we can successfully train an SNN to perform angular velocity regression.
  arXiv  Detail & Related papers  (2020-03-05T17:37:16Z)

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.