Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff

• URL: http://arxiv.org/abs/2301.09522v4
• Date: Sun, 02 Feb 2025 09:21:54 GMT
• Title: Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff
• Authors: Dengyu Wu, Gaojie Jin, Han Yu, Xinping Yi, Xiaowei Huang,
• Abstract summary: Spiking neural network (SNN) offer a closer mimicry of natural neural networks. Current SNN is trained to infer over a fixed duration. We propose a cutoff in SNN, which can terminate SNN anytime during inference to achieve efficient inference.
• Score: 31.61525648918492
• License:
• Abstract: Spiking neural network (SNN), as the next generation of artificial neural network (ANN), offer a closer mimicry of natural neural networks and hold promise for significant improvements in computational efficiency. However, the current SNN is trained to infer over a fixed duration, overlooking the potential of dynamic inference in SNN. In this paper, we strengthen the marriage between SNN and event-driven processing with a proposal to consider a cutoff in SNN, which can terminate SNN anytime during inference to achieve efficient inference. Two novel optimisation techniques are presented to achieve inference efficient SNN: a Top-K cutoff and a regularisation.The proposed regularisation influences the training process, optimising SNN for the cutoff, while the Top-K cutoff technique optimises the inference phase. We conduct an extensive set of experiments on multiple benchmark frame-based datasets, such asCIFAR10/100, Tiny-ImageNet, and event-based datasets, including CIFAR10-DVS, N-Caltech101 and DVS128 Gesture. The experimental results demonstrate the effectiveness of our techniques in both ANN-to-SNN conversion and direct training, enabling SNNs to require 1.76 to 2.76x fewer timesteps for CIFAR-10, while achieving 1.64 to 1.95x fewer timesteps across all event-based datasets, with near-zero accuracy loss. These findings affirms the compatibility and potential benefits of our techniques in enhancing accuracy and reducing inference latency when integrated with existing methods. Code available: https://github.com/Dengyu-Wu/SNNCutoff

Related papers

• Reducing ANN-SNN Conversion Error through Residual Membrane Potential [19.85338979292052]
  Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips. In this paper, we make a detailed analysis of unevenness error and divide it into four categories. We propose an optimization strategy based on residual membrane potential to reduce unevenness error.
  arXiv  Detail & Related papers  (2023-02-04T04:44:31Z)
• 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)
• Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks [64.23331120621118]
  We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs) INNs are a class of implicit learning models that use implicit equations as layers. We show that our approach performs at least as well as, and generally better than, applying state-of-the-art interval bound propagation methods to INNs.
  arXiv  Detail & Related papers  (2022-04-01T03:31:27Z)
• Rethinking Pretraining as a Bridge from ANNs to SNNs [13.984523794353477]
  Spiking neural networks (SNNs) are known as a typical kind of brain-inspired models with their unique features. How to obtain a high-accuracy model has always been the main challenge in the field of SNN.
  arXiv  Detail & Related papers  (2022-03-02T14:59:57Z)
• Optimized Potential Initialization for Low-latency Spiking Neural Networks [21.688402090967497]
  Spiking Neural Networks (SNNs) have been attached great importance due to the distinctive properties of low power consumption, biological plausibility, and adversarial robustness. The most effective way to train deep SNNs is through ANN-to-SNN conversion, which have yielded the best performance in deep network structure and large-scale datasets. In this paper, we aim to achieve high-performance converted SNNs with extremely low latency (fewer than 32 time-steps)
  arXiv  Detail & Related papers  (2022-02-03T07:15:43Z)
• 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)
• Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks [72.81092567651395]
  Sub-bit Neural Networks (SNNs) are a new type of binary quantization design tailored to compress and accelerate BNNs. SNNs are trained with a kernel-aware optimization framework, which exploits binary quantization in the fine-grained convolutional kernel space. Experiments on visual recognition benchmarks and the hardware deployment on FPGA validate the great potentials of SNNs.
  arXiv  Detail & Related papers  (2021-10-18T11:30:29Z)
• Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning [60.20150317299749]
  This paper proposes a deep time delay neural network (TDNN) for speech enhancement with full data learning. To make full use of the training data, we propose a full data learning method for speech enhancement.
  arXiv  Detail & Related papers  (2020-11-11T06:32:37Z)
• 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)
• Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation [10.972663738092063]
  Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes) We present a computationally-efficient training technique for deep SNNs. We achieve top-1 accuracy of 65.19% for ImageNet dataset on SNN with 250 time steps, which is 10X faster compared to converted SNNs with similar accuracy.
  arXiv  Detail & Related papers  (2020-05-04T19:30:43Z)

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.