Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation

• URL: http://arxiv.org/abs/2408.09108v1
• Date: Sat, 17 Aug 2024 06:23:38 GMT
• Title: Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation
• Authors: Lin Zuo, Yongqi Ding, Wenwei Luo, Mengmeng Jing, Xianlong Tian, Kunshan Yang,
• Abstract summary: Spi neural networks (SNNs) have received widespread attention as an ultra-low energy computing paradigm. Recent studies have focused on improving the feature extraction capability of SNNs, but they suffer from inefficient and suboptimal performance. We. propose a simple yet effective temporal reversed training (TRT) method to optimize the temporal performance of SNNs.
• Score: 3.5624857747396814
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Spiking neural networks (SNNs) have received widespread attention as an ultra-low energy computing paradigm. Recent studies have focused on improving the feature extraction capability of SNNs, but they suffer from inefficient inference and suboptimal performance. In this paper, we propose a simple yet effective temporal reversed training (TRT) method to optimize the spatio-temporal performance of SNNs and circumvent these problems. We perturb the input temporal data by temporal reversal, prompting the SNN to produce original-reversed consistent output logits and to learn perturbation-invariant representations. For static data without temporal dimension, we generalize this strategy by exploiting the inherent temporal property of spiking neurons for spike feature temporal reversal. In addition, we utilize the lightweight ``star operation" (element-wise multiplication) to hybridize the original and temporally reversed spike firing rates and expand the implicit dimensions, which serves as spatio-temporal regularization to further enhance the generalization of the SNN. Our method involves only an additional temporal reversal operation and element-wise multiplication during training, thus incurring negligible training overhead and not affecting the inference efficiency at all. Extensive experiments on static/neuromorphic object/action recognition, and 3D point cloud classification tasks demonstrate the effectiveness and generalizability of our method. In particular, with only two timesteps, our method achieves 74.77\% and 90.57\% accuracy on ImageNet and ModelNet40, respectively.

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)
• The Role of Temporal Hierarchy in Spiking Neural Networks [2.0881857682885836]
  Spiking Neural Networks (SNNs) have the potential for rich-temporal signal processing thanks to exploiting both spatial and temporal parameters. Time constants have been recently shown to have computational benefits that help reduce the overall number of parameters required in the network. To reduce the cost of optimization, architectural biases can be applied, in this case in the temporal domain. We propose to impose a hierarchy of temporal representation in the hidden layers of SNNs, highlighting that such an inductive bias improves their performance.
  arXiv  Detail & Related papers  (2024-07-26T16:00:20Z)
• FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion [16.9748086865693]
  Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs) In this work, we introduce a lightweight Forward Temporal Bias (FTBC) technique, aimed at enhancing conversion accuracy without the computational overhead. We further propose an algorithm for finding the temporal bias only in the forward pass, thus eliminating the computational burden of backpropagation.
  arXiv  Detail & Related papers  (2024-03-27T09:25:20Z)
• Deep Multi-Threshold Spiking-UNet for Image Processing [51.88730892920031]
  This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two primary challenges: ensuring high-fidelity information propagation through the network via spikes and formulating an effective training strategy. Experimental results show that, on image segmentation and denoising, our Spiking-UNet achieves comparable performance to its non-spiking counterpart.
  arXiv  Detail & Related papers  (2023-07-20T16:00:19Z)
• Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistency [9.545711665562715]
  Spiking neural networks have gained significant attention due to their brain-like information processing capabilities. Current training algorithms tend to overlook the differences in output distribution at various timesteps. We have designed a method to enhance the temporal consistency of outputs at different timesteps.
  arXiv  Detail & Related papers  (2023-05-23T15:50:07Z)
• Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks [70.75043144299168]
  Spiking Neural Networks (SNNs) are promising energy-efficient models for neuromorphic computing. We propose the Spatial Learning Through Time (SLTT) method that can achieve high performance while greatly improving training efficiency. Our method achieves state-of-the-art accuracy on ImageNet, while the memory cost and training time are reduced by more than 70% and 50%, respectively, compared with BPTT.
  arXiv  Detail & Related papers  (2023-02-28T05:01:01Z)
• Energy Efficient Training of SNN using Local Zeroth Order Method [18.81001891391638]
  Spiking neural networks are becoming increasingly popular for their low energy requirement in real-world tasks. SNN training algorithms face the loss of gradient information and non-differentiability due to the Heaviside function. We propose a differentiable approximation of the Heaviside in the backward pass, while the forward pass uses the Heaviside as the spiking function.
  arXiv  Detail & Related papers  (2023-02-02T06:57:37Z)
• 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)
• Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networks [5.484391472233163]
  The success of deep learning is inseparable from backpropagation. We propose a biological plausible spatial adjustment, which rethinks the relationship between membrane potential and spikes. Secondly, we propose a biologically plausible temporal adjustment making the error propagate across the spikes in the temporal dimension.
  arXiv  Detail & Related papers  (2021-10-17T15:55:51Z)
• Time Dependence in Non-Autonomous Neural ODEs [74.78386661760662]
  We propose a novel family of Neural ODEs with time-varying weights. We outperform previous Neural ODE variants in both speed and representational capacity.
  arXiv  Detail & Related papers  (2020-05-05T01:41:46Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

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.