Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Summary

This paper introduces Mamba, a novel state space model (SSM) for sequence modeling that achieves linear-time processing of sequences. Mamba addresses the limitations of existing SSMs by incorporating a hardware-aware design that efficiently utilizes modern accelerators. It accomplishes this through a selective state space model that filters and processes input sequences based on their relevance, leading to significant improvements in performance and efficiency. The authors demonstrate Mamba's capabilities through extensive experiments on a range of sequence modeling tasks, including language modeling, audio generation, and genomics data analysis. The results show that Mamba outperforms existing state-of-the-art models in terms of both speed and accuracy, particularly on long sequences. The paper highlights the importance of hardware-aware design in achieving optimal performance in sequence modeling and provides a new baseline for future research in this area.

Key Takeaways

1. Mamba introduces a selective state space model architecture, enabling efficient sequence processing.
2. The design of Mamba is hardware-aware, optimizing for modern accelerators and parallel computation.
3. Mamba achieves linear-time complexity, improving processing speed compared to quadratic-time transformers, especially for long sequences.
4. The research demonstrates strong performance gains across diverse sequence modeling tasks including language modeling, audio generation, and genomics.