[공지사항]을 빙자한 안부와 근황 
Show more

Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

Characterizing State Space Model (SSM) and SSM-Transformer Hybrid Language Model Performance with Long Context Length

Created by
  • Haebom

Author

Saptarshi Mitra, Rachid Karami, Haocheng Xu, Sitao Huang, Hyoukjun Kwon

Outline

This paper presents research to overcome the limitations of the existing Transformer architecture due to the increasing demand for machine intelligence that can process continuous and long-term contextual inputs on local devices. To solve the problems of low efficiency and practical use due to the secondary complexity and memory requirements of the existing Transformer, research is being conducted focusing on State Space Models (SSMs) and hybrid models that provide linear scalability. In this paper, we perform comprehensive comparative benchmarking of Transformer, SSM, and hybrid models for long-term context inference on real consumer and embedded GPUs, and show that SSM is more suitable for long-term context processing and can process up to 220K tokens on consumer GPUs. In particular, we confirm that SSM is up to 4 times faster than Transformer in long-term contexts, and reveal that the hardware-aware SSM kernel accounts for more than 55% of the inference execution time, suggesting that it is a key target for future hardware acceleration. In addition, we provide detailed device-specific characteristic analysis results for edge system co-design, and we plan to open-source the benchmarking framework to further advance our research.

Takeaways, Limitations

•
Takeaways:
◦
We experimentally demonstrate that SSM-based models are more efficient and perform better than Transformer in long-term context inference.
◦
System-level optimizations for long-context inference and new directions for application development.
◦
Presenting the SSM kernel as a primary target for hardware acceleration.
◦
Suggesting the potential to improve long-context processing performance on edge devices.
◦
Facilitating follow-up research by providing an open source benchmarking framework.
•
Limitations:
◦
Because this study is based on benchmarking results for specific consumer and embedded GPUs, generalizability to other hardware platforms may be limited.
◦
Rather than a comprehensive comparison of various SSM architectures and hybrid models, the study was conducted on a limited set of models.
◦
Focusing solely on the performance aspect may result in a lack of analysis of model accuracy or other important aspects.
👍
Made with Slashpage