On Reducing Activity with Distillation and Regularization for Energy Efficient Spiking Neural Networks

• URL: http://arxiv.org/abs/2406.18350v1
• Date: Wed, 26 Jun 2024 13:51:57 GMT
• Title: On Reducing Activity with Distillation and Regularization for Energy Efficient Spiking Neural Networks
• Authors: Thomas Louis, Benoit Miramond, Alain Pegatoquet, Adrien Girard,
• Abstract summary: Interest in spiking neural networks (SNNs) has been growing steadily, promising an energy-efficient alternative to formal neural networks (FNNs) We propose to leverage Knowledge Distillation (KD) for SNNs training with surrogate gradient descent in order to optimize the trade-off between performance and spiking activity.
• Score: 0.19999259391104385
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Interest in spiking neural networks (SNNs) has been growing steadily, promising an energy-efficient alternative to formal neural networks (FNNs), commonly known as artificial neural networks (ANNs). Despite increasing interest, especially for Edge applications, these event-driven neural networks suffered from their difficulty to be trained compared to FNNs. To alleviate this problem, a number of innovative methods have been developed to provide performance more or less equivalent to that of FNNs. However, the spiking activity of a network during inference is usually not considered. While SNNs may usually have performance comparable to that of FNNs, it is often at the cost of an increase of the network's activity, thus limiting the benefit of using them as a more energy-efficient solution. In this paper, we propose to leverage Knowledge Distillation (KD) for SNNs training with surrogate gradient descent in order to optimize the trade-off between performance and spiking activity. Then, after understanding why KD led to an increase in sparsity, we also explored Activations regularization and proposed a novel method with Logits Regularization. These approaches, validated on several datasets, clearly show a reduction in network spiking activity (-26.73% on GSC and -14.32% on CIFAR-10) while preserving accuracy.

Related papers

• Fully Spiking Actor Network with Intra-layer Connections for Reinforcement Learning [51.386945803485084]
  We focus on the task where the agent needs to learn multi-dimensional deterministic policies to control. Most existing spike-based RL methods take the firing rate as the output of SNNs, and convert it to represent continuous action space (i.e., the deterministic policy) through a fully-connected layer. To develop a fully spiking actor network without any floating-point matrix operations, we draw inspiration from the non-spiking interneurons found in insects.
  arXiv  Detail & Related papers  (2024-01-09T07:31:34Z)
• 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)
• Skip Connections in Spiking Neural Networks: An Analysis of Their Effect on Network Training [0.8602553195689513]
  Spiking neural networks (SNNs) have gained attention as a promising alternative to traditional artificial neural networks (ANNs) In this paper, we study the impact of skip connections on SNNs and propose a hyper parameter optimization technique that adapts models from ANN to SNN. We demonstrate that optimizing the position, type, and number of skip connections can significantly improve the accuracy and efficiency of SNNs.
  arXiv  Detail & Related papers  (2023-03-23T07:57:32Z)
• Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks [19.490903216456758]
  Spiking neural networks (SNNs) are neural networks with asynchronous discrete and sparse characteristics. We propose a multi-level firing (MLF) method based on the existing spiking-suppressed residual network (spiking DS-ResNet)
  arXiv  Detail & Related papers  (2022-10-12T16:39:46Z)
• 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)
• Pruning of Deep Spiking Neural Networks through Gradient Rewiring [41.64961999525415]
  Spiking Neural Networks (SNNs) have been attached great importance due to their biological plausibility and high energy-efficiency on neuromorphic chips. Most existing methods directly apply pruning approaches in artificial neural networks (ANNs) to SNNs, which ignore the difference between ANNs and SNNs. We propose gradient rewiring (Grad R), a joint learning algorithm of connectivity and weight for SNNs, that enables us to seamlessly optimize network structure without retrain.
  arXiv  Detail & Related papers  (2021-05-11T10:05:53Z)
• Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks [1.8515971640245998]
  Spiking neural networks (SNNs) have been investigated as more biologically plausible and potentially more powerful models of neural computation. We show how a novel surrogate gradient combined with recurrent networks of tunable and adaptive spiking neurons yields state-of-the-art for SNNs.
  arXiv  Detail & Related papers  (2021-03-12T10:27:29Z)
• 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)
• You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference to ANN-Level Accuracy [51.861168222799186]
  Spiking Neural Networks (SNNs) are a type of neuromorphic, or brain-inspired network. SNNs are sparse, accessing very few weights, and typically only use addition operations instead of the more power-intensive multiply-and-accumulate operations. In this work, we aim to overcome the limitations of TTFS-encoded neuromorphic systems.
  arXiv  Detail & Related papers  (2020-06-03T15:55:53Z)
• T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding [26.654533157221973]
  This paper introduces the concept of time-to-first-spike coding into deep SNNs using the kernel-based dynamic threshold and dendrite to overcome the drawback. According to our results, the proposed methods can reduce inference latency and number of spikes to 22% and less than 1%, compared to those of burst coding.
  arXiv  Detail & Related papers  (2020-03-26T04:39:12Z)

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.