Our department’s Professor Myounsoo Jung’s research team has developed the world’s first AI semiconductor for search engines based on CXL 3.0.
Approximate nearest neighbor search (ANNS) is widely used in commercial services such as image search, database, and recommendation systems.
However, in production-level ANNS, there is a challenge of requiring a large amount of memory due to the extensive dataset.
To address this memory pressure issue, modern ANNS techniques leverage lossy compression methods or employ persistent storage for their memory expansion.
However, these approaches often suffer from low accuracy and performance.
The research team proposed expanding memory capacity via compute express link (CXL), which is PCIe based open-industry interconnect technology that allows the underlying working memory to be highly scalable and composable at a low cost.
Furthermore, the use of a CXL switch enables connecting multiple memory expanders to a single port, providing greater scalability. However, memory expansion through CXL has the disadvantage of increased memory access time compared to local memory.
The research team has developed an AI semiconductor, ‘CXL-ANNS‘, which leverages CXL switch and memory expanders to accommodate high memory pressure that comes from extensive datasets without losing accuracy or performance.
Additionally, by using near data processing and data partitioning based on locality, the performance of CXL-ANNS is improved.
They also compared prototyped CXL-ANNS with the existing solutions for ANNS. Compared to previous research, CXL-ANNS shows 111 times higher performance. Particularly, 92 times higher performance can be achieved compared to Microsoft’s solution that is used in commercial service.
This research, along with the paper titled “CXL-ANNS: Software-Hardware Collaborative Memory Disaggregation and Computation for Billion-Scale Approximate Nearest Neighbor Search”, will be presented in July at ‘USENIX Annual Technical Conference, ATC, 2023’.
The research was supported by Panmnesia (http://panmnesia.com). More information on this paper can be found at CAMELab website (http://camelab.org).
The Korea Economic Daily: https://www.hankyung.com/it/article/202305259204i
The Herald Business: http://news.heraldcorp.com/view.php?ud=20230525000225