Login
- Titel:
SpikeDecoder: Realizing the GPT Architecture with Spiking Neural Networks
- Dokumenttyp:
- Zeitschriftenaufsatz
- Autor(en):
- Beger, Claas; Walter, Florian; Knoll, Alois
- Jahr:
- 2026
- WWW:
- https://arxiv.org/abs/2606.12287
- Hinweise:
- 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 trai...
BibTeX