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Publications of SPCL

W. Jiang, S. Li, Y. Zhu, J. de Fine Licht, Z. He, R. Shi, C. Renggli, S. Zhang, T. Rekatsinas, T. Hoefler, G. Alonso:
	Co-design Hardware and Algorithm for Vector Search (In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'23), presented in Denver, CO, USA, Association for Computing Machinery, ISBN: 979-8-400701-09-2, Nov. 2023) Publisher Reference Abstract Vector search has emerged as the foundation for large-scale information retrieval and machine learning systems, with search engines like Google and Bing processing tens of thousands of queries per second on petabyte-scale document datasets by evaluating vector similarities between encoded query texts and web documents. As performance demands for vector search systems surge, accelerated hardware offers a promising solution in the post-Moore's Law era. We introduce extit{FANNS}, an end-to-end and scalable vector search framework on FPGAs. Given a user-provided recall requirement on a dataset and a hardware resource budget, extit{FANNS} automatically co-designs hardware and algorithm, subsequently generating the corresponding accelerator. The framework also supports scale-out by incorporating a hardware TCP/IP stack in the accelerator. extit{FANNS} attains up to 23.0 Documents download article: access preprint on arxiv:
	BibTeX
@inproceedings{,   author={Wenqi Jiang and Shigang Li and Yu Zhu and Johannes de Fine Licht and Zhenhao He and Runbin Shi and Cedric Renggli and Shuai Zhang and Theodoros Rekatsinas and Torsten Hoefler and Gustavo Alonso},   title={{Co-design Hardware and Algorithm for Vector Search}},   year={2023},   month={11},   booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'23)},   location={Denver, CO, USA},   publisher={Association for Computing Machinery},   isbn={979-8-400701-09-2},   doi={10.1145/3581784.3607099}, }