AI is advancing rapidly, with Transformers playing a key role. However, State Space Models (SSMs) like Mamba offer a viable alternative to Transformers by addressing inefficiencies. Mamba boasts speed, scalability, and comparable performance at long sequence lengths, outperforming Transformers in various tasks. The use of Control Theory-inspired SSM in Mamba allows for efficient communication between tokens and computation within tokens, enhancing effectiveness and efficiency. The innovative Selection Mechanism in Mamba enables context-dependent reasoning, focusing, and ignoring, enhancing performance and interpretability. By dynamically compressing data into a small, focused state, Mamba pushes the boundaries of effectiveness and efficiency in AI models.
https://www.kolaayonrinde.com/blog/2024/02/11/mamba.html