Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks

• URL: http://arxiv.org/abs/2005.11633v2
• Date: Tue, 16 Jun 2020 14:12:49 GMT
• Title: Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks
• Authors: Bojian Yin, Federico Corradi, Sander M. Boht\'e
• Abstract summary: We show how a novel type of adaptive spiking recurrent neural network (SRNN) is able to achieve state-of-the-art performance. We calculate a $>$100x energy improvement for our SRNNs over classical RNNs on the harder tasks.
• Score: 0.9790524827475205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is motivating the search for high-performance and efficient spiking neural networks to run on this hardware. However, compared to classical neural networks in deep learning, current spiking neural networks lack competitive performance in compelling areas. Here, for sequential and streaming tasks, we demonstrate how a novel type of adaptive spiking recurrent neural network (SRNN) is able to achieve state-of-the-art performance compared to other spiking neural networks and almost reach or exceed the performance of classical recurrent neural networks (RNNs) while exhibiting sparse activity. From this, we calculate a $>$100x energy improvement for our SRNNs over classical RNNs on the harder tasks. To achieve this, we model standard and adaptive multiple-timescale spiking neurons as self-recurrent neural units, and leverage surrogate gradients and auto-differentiation in the PyTorch Deep Learning framework to efficiently implement backpropagation-through-time, including learning of the important spiking neuron parameters to adapt our spiking neurons to the tasks.

Related papers

• Channel-wise Parallelizable Spiking Neuron with Multiplication-free Dynamics and Large Temporal Receptive Fields [32.349167886062105]
  Spiking Neural Networks (SNNs) are distinguished from Artificial Neural Networks (ANNs) for their sophisticated neuronal dynamics and sparse binary activations (spikes) inspired by the biological neural system. Traditional neuron models use iterative step-by-step dynamics, resulting in serial computation and slow training speed of SNNs. Recent parallelizable spiking neuron models have been proposed to fully utilize the massive parallel computing ability of graphics processing units to accelerate the training of SNNs.
  arXiv  Detail & Related papers  (2025-01-24T13:44:08Z)
• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval. A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing [16.60622265961373]
  Spiking Neural Networks (SNNs) represent the forefront of neuromorphic computing. This paper weaves together three groundbreaking studies that revolutionize SNN performance.
  arXiv  Detail & Related papers  (2024-07-08T23:33:12Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Expressivity of Spiking Neural Networks [15.181458163440634]
  We study the capabilities of spiking neural networks where information is encoded in the firing time of neurons. In contrast to ReLU networks, we prove that spiking neural networks can realize both continuous and discontinuous functions.
  arXiv  Detail & Related papers  (2023-08-16T08:45:53Z)
• 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)
• Spiking neural network for nonlinear regression [68.8204255655161]
  Spiking neural networks carry the potential for a massive reduction in memory and energy consumption. They introduce temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware. A framework for regression using spiking neural networks is proposed.
  arXiv  Detail & Related papers  (2022-10-06T13:04:45Z)
• 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)
• Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification [1.8411688477000185]
  spiking neural networks (SNNs) that more closely resemble biological brain synapses have attracted attention owing to their low power consumption. We build versatile hybrid neural networks (HNNs) that improve the concerned performance.
  arXiv  Detail & Related papers  (2021-02-21T12:03:16Z)
• 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)
• Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data [77.92736596690297]
  We introduce a novel training strategy that allows learning not only the input-output behavior of an RNN but also its internal network dynamics. We test the proposed method by training an RNN to simultaneously reproduce internal dynamics and output signals of a physiologically-inspired neural model. Remarkably, we show that the reproduction of the internal dynamics is successful even when the training algorithm relies on the activities of a small subset of neurons.
  arXiv  Detail & Related papers  (2020-05-05T14:16:54Z)

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.