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Related Experiment Video

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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SpikeBERT: A language spikformer learned from BERT with knowledge distillation.

Changze Lv¹, Tianlong Li¹, Weiming Qiao¹

¹School of Computer Science, Fudan University, Shanghai, China; Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China.

Neural Networks : the Official Journal of the International Neural Network Society

|December 25, 2025

Summary

SpikeBERT, a novel spiking neural network (SNN) approach, achieves BERT-level performance on language tasks with significantly reduced energy consumption. This method enhances deep SNNs for natural language processing.

Keywords:

BERT Knowledge distillation Language understanding Spiking neural networks

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Area of Science:

Artificial Intelligence
Computational Neuroscience
Natural Language Processing

Background:

Spiking neural networks (SNNs) offer energy-efficient deep learning but lag in language task performance due to shallow architectures.
Existing SNNs for language tasks exhibit a performance gap compared to transformer models like BERT.

Purpose of the Study:

To enhance Spikformer, a spiking Transformer, for advanced language task processing.
To develop a two-stage knowledge distillation method for training deep SNNs.

Main Methods:

Improved Spikformer architecture for language tasks.
Two-stage knowledge distillation: BERT pre-training and fine-tuning.
Training SpikeBERT using large unlabelled and task-specific datasets.

Main Results:

SpikeBERT achieves state-of-the-art performance among SNNs.
Comparable results to BERT on English and Chinese text classification.
Demonstrates significantly lower energy consumption than BERT.

Conclusions:

The proposed method effectively trains deep SNNs for language tasks.
SpikeBERT offers a viable, energy-efficient alternative to transformer models for NLP.
This research bridges the performance gap between SNNs and transformers in language processing.