Show HN: A concept implementation for a faster Transformer [pdf] (github.com)

A new concept implementation called Tauformer, the Topological Transformer, has been introduced, aiming to significantly enhance the speed of Transformer models. This implementation leverages a Permanent Domain Memory system combined with an optimized Key-Value (KV) cache to improve efficiency during processing, a critical challenge typically faced by traditional Transformer architectures. The key highlights include substantial speed improvements and reduced computation times, which could enable faster training and inference. The significance of Tauformer for the AI/ML community lies in its potential to address the growing demand for rapid and efficient processing in large language models (LLMs). As these models continue to scale, any advancement that reduces computational resource requirements while maintaining performance is pivotal. The innovative use of topological structures in memory management could lead to wider adoption in various applications, from real-time language translation to advanced conversational agents, setting a new benchmark for future AI model designs.