Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866909550432485376 |
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| author | Jiang, Wenqi Zeller, Marco Waleffe, Roger Hoefler, Torsten Alonso, Gustavo |
| author_facet | Jiang, Wenqi Zeller, Marco Waleffe, Roger Hoefler, Torsten Alonso, Gustavo |
| contents | A Retrieval-Augmented Language Model (RALM) combines a large language model (LLM) with a vector database to retrieve context-specific knowledge during text generation. This strategy facilitates impressive generation quality even with smaller models, thus reducing computational demands by orders of magnitude. To serve RALMs efficiently and flexibly, we propose Chameleon, a heterogeneous accelerator system integrating both LLM and vector search accelerators in a disaggregated architecture. The heterogeneity ensures efficient serving for both inference and retrieval, while the disaggregation allows independent scaling of LLM and vector search accelerators to fulfill diverse RALM requirements. Our Chameleon prototype implements vector search accelerators on FPGAs and assigns LLM inference to GPUs, with CPUs as cluster coordinators. Evaluated on various RALMs, Chameleon exhibits up to 2.16$\times$ reduction in latency and 3.18x speedup in throughput compared to the hybrid CPU-GPU architecture. The promising results pave the way for adopting heterogeneous accelerators for not only LLM inference but also vector search in future RALM systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_09949 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models Jiang, Wenqi Zeller, Marco Waleffe, Roger Hoefler, Torsten Alonso, Gustavo Machine Learning Artificial Intelligence Hardware Architecture Computation and Language A Retrieval-Augmented Language Model (RALM) combines a large language model (LLM) with a vector database to retrieve context-specific knowledge during text generation. This strategy facilitates impressive generation quality even with smaller models, thus reducing computational demands by orders of magnitude. To serve RALMs efficiently and flexibly, we propose Chameleon, a heterogeneous accelerator system integrating both LLM and vector search accelerators in a disaggregated architecture. The heterogeneity ensures efficient serving for both inference and retrieval, while the disaggregation allows independent scaling of LLM and vector search accelerators to fulfill diverse RALM requirements. Our Chameleon prototype implements vector search accelerators on FPGAs and assigns LLM inference to GPUs, with CPUs as cluster coordinators. Evaluated on various RALMs, Chameleon exhibits up to 2.16$\times$ reduction in latency and 3.18x speedup in throughput compared to the hybrid CPU-GPU architecture. The promising results pave the way for adopting heterogeneous accelerators for not only LLM inference but also vector search in future RALM systems. |
| title | Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models |
| topic | Machine Learning Artificial Intelligence Hardware Architecture Computation and Language |
| url | https://arxiv.org/abs/2310.09949 |