Nd-BiMamba2: A Unified Bidirectional Architecture for Multi-Dimensional Data Processing (2411.15380v1)

Abstract: Deep learning models often require specially designed architectures to process data of different dimensions, such as 1D time series, 2D images, and 3D volumetric data. Existing bidirectional models mainly focus on sequential data, making it difficult to scale effectively to higher dimensions. To address this issue, we propose a novel multi-dimensional bidirectional neural network architecture, named Nd-BiMamba2, which efficiently handles 1D, 2D, and 3D data. Nd-BiMamba2 is based on the Mamba2 module and introduces innovative bidirectional processing mechanisms and adaptive padding strategies to capture bidirectional information in multi-dimensional data while maintaining computational efficiency. Unlike existing methods that require designing specific architectures for different dimensional data, Nd-BiMamba2 adopts a unified architecture with a modular design, simplifying development and maintenance costs. To verify the portability and flexibility of Nd-BiMamba2, we successfully exported it to ONNX and TorchScript and tested it on different hardware platforms (e.g., CPU, GPU, and mobile devices). Experimental results show that Nd-BiMamba2 runs efficiently on multiple platforms, demonstrating its potential in practical applications. The code is open-source: https://github.com/Human9000/nd-Mamba2-torch

• The paper introduces Nd-BiMamba2, a unified architecture extending Mamba2 for efficient bidirectional processing of 1D, 2D, and 3D data using adaptive padding.
• Empirical results show Nd-BiMamba2 improves feature representation over unidirectional models, particularly in 3D contexts, despite increased computational demands.
• Nd-BiMamba2 offers significant potential for cross-dimensional applications in fields like NLP, computer vision, and volumetric analysis due to its flexibility and efficiency.

Overview of Nd-BiMamba2: A Unified Bidirectional Architecture for Multi-Dimensional Data Processing

The publication under discussion, "Nd-BiMamba2: A Unified Bidirectional Architecture for Multi-Dimensional Data Processing," authored by Hao Liu, presents a significant contribution to the field of deep learning, particularly in handling multi-dimensional data. The advanced architecture, Nd-BiMamba2, offers a unified solution for efficiently processing 1D, 2D, and 3D data—overcoming limitations inherent in existing unidirectional and dimension-specific models.

Key Innovations and Contributions

Nd-BiMamba2 extends the Mamba2 module, introducing a novel bidirectional processing mechanism optimized for handling diverse datasets across varying dimensions. This architecture leverages adaptive padding strategies that enhance computational efficiency while minimizing memory consumption—key issues in high-dimensional data processing. The method proposes the following innovations:

• Extension to support efficient bidirectional processing applicable to 1D, 2D, and 3D data.
• An adaptive padding strategy that dynamically adjusts based on input data dimensions.
• A unified architecture that eschews the need for dimensional-specific model designs, showcasing adaptability across platforms by supporting export to ONNX and TorchScript.

Empirical Validation

Experiments conducted demonstrate the performance efficacy and flexibility of Nd-BiMamba2 on multiple hardware platforms (e.g., CPUs, GPUs, and mobile devices). The empirical results indicate superior feature representation capabilities, particularly with the bidirectional modeling module that captures both forward and backward information flows. Comparative experiments against traditional unidirectional models reveal improvements in feature extraction and representation, with noticeable differences in datasets requiring extensive feature exploration.

Numerical Performance

The paper provides detailed numerical evaluations wherein enabling bidirectional modeling results in a significant increase in computational overhead, evidenced by an increase in FLOPs and time requirements. However, Nd-BiMamba2 compensates with marked improvements in feature richness and model expression power, particularly in 3D data contexts. The performance advantages are evidenced by comparisons with other models, such as BiLSTM and Transformer-based models, which underscore Nd-BiMamba2's efficiency and modular adaptability despite increased computational demands.

Implications and Future Directions

The implications of Nd-BiMamba2 are profound in the field of cross-dimensional deep learning applications. This architecture offers robust potential across multiple domains, such as natural language processing, computer vision, and volumetric data analysis, where computational efficiency and flexibility are paramount. Future research may explore further enhancements in bidirectional processing mechanisms or develop additional optimizations tailored to even higher-dimensional data scenarios.

The framework also paves the way for advancements in multi-modal data fusion, potentially enhancing integration with self-attention models like Transformers by addressing their computational limitations when faced with high-dimensional data. Future iterations could explore integrating Nd-BiMamba2 with emerging architectures to further ameliorate the balance between efficiency and feature retention, thereby opening new avenues for scalable deep learning applications.

In summary, Nd-BiMamba2 significantly advances bidirectional neural network methodologies, showcasing the feasibility and utility of a unified architecture applicable across a spectrum of data dimensions while delivering substantial computational and adaptational benefits.