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State space models (SSMs) combined with spiking neural networks (SNNs) show promise for energy-efficient long-range sequence modeling. This approach outperforms Transformers and current SNNs on key benchmarks, paving the way for efficient large language models on neuromorphic hardware.

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Machine Learning

Background:

  • Spiking neural networks (SNNs) offer energy-efficient computation but lag behind Transformers in sequential tasks due to RNN limitations and training challenges.
  • State space models (SSMs) have emerged as efficient alternatives to Transformers for sequence modeling.

Purpose of the Study:

  • To investigate the integration of state-of-the-art SSMs with SNNs for long-range sequence modeling.
  • To evaluate the performance of SSM-based SNNs against Transformers and existing SNNs.

Main Methods:

  • Systematic investigation of SSM-SNN intersection for long-range sequence modeling.
  • Introduction of a novel feature mixing layer to enhance SNN accuracy.
  • Benchmarking against established long-range sequence modeling tasks and sequential image classification.

Main Results:

  • SSM-based SNNs outperformed Transformer models on all tasks in a long-range sequence modeling benchmark.
  • SSM-based SNNs achieved superior performance compared to state-of-the-art SNNs with fewer parameters in sequential image classification.
  • A novel feature mixing layer improved SNN accuracy, questioning prior assumptions about binary activations.

Conclusions:

  • SSM-based SNNs represent a significant advancement for energy-efficient long-range sequence modeling.
  • This research enables the deployment of powerful SSM architectures, like large language models, on neuromorphic hardware.
  • The findings open new avenues for efficient and brain-inspired AI.