SpikeDecoder: Realizing the GPT Architecture with Spiking Neural Networks
The Transformer architecture is widely regarded as the most powerful tool for natural language processing, but due to a high number of complex operations, it inherently faces the issue of high energy consumption. To address this issue, we consider Spiking Neural Networks (SNNs), which are an energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their naturally event-driven approach to processing information. However, this inherently makes them difficult to train. Often, many SNN-based models circumvent this issue by converting pre-trained ANNs. More recently, attempts have been made to design directly trainable SNN-based adaptations of the Transformer model...
| Authors | Claas Beger, Florian Walter, Alois Knoll |
|---|---|
| Primary category | cs.NE |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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