| Title | Accelerating Large-Scale Nearest Neighbor Search with Computational Storage Device |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Kim, Ji-Hoon, Park, Yeo-Reum, Do, Jaeyoung, Ji, Soo-Young, Kim, Joo-Young |
| Conference Name | 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) |
| Keywords | Bandwidth, Computational efficiency, Computational Storage Device, FPGA, image retrieval, machine learning, machine learning algorithms, Measurement, Metrics, Near Data Processing, Nearest neighbor methods, nearest neighbor search, pubcrawl, SSD, Task Analysis |
| Abstract | K-nearest neighbor algorithm that searches the K closest samples in a high dimensional feature space is one of the most fundamental tasks in machine learning and image retrieval applications. Computational storage device that combines computing unit and storage module on a single board becomes popular to address the data bandwidth bottleneck of the conventional computing system. In this paper, we propose a nearest neighbor search acceleration platform based on computational storage device, which can process a large-scale image dataset efficiently in terms of speed, energy, and cost. We believe that the proposed acceleration platform is promising to be deployed in cloud datacenters for data-intensive applications. |
| DOI | 10.1109/FCCM51124.2021.00041 |
| Citation Key | kim_accelerating_2021 |
Accelerating Large-Scale Nearest Neighbor Search with Computational Storage Device