Serving Deep Learning Models from Relational Databases
Lixi Zhou, Qi Lin, et al.
EDBT 2024
Businesses are experiencing a growing need for performing real-time analytics on ever-increasing enterprise data. While providing useful business insights and improved market responsiveness, analytics also adds a computational burden on traditional online transaction processing (OLTP) systems and could adversely affect the performance of OLTP workloads. The authors present a highly pipelined, high-throughput query-processing engine on a field-programmable gate array (FPGA) to offload expensive queries for database analytics. The proposed solution provides a mechanism for a database management system (DBMS) to seamlessly harness the FPGA accelerator without requiring any changes in the application or the existing data layout. The authors' system, which uses an off-the-shelf server and a PCIe-attached FPGA card and is integrated into a commercial DBMS platform, achieves an order-of-magnitude speedup on various real-life queries. © 1981-2012 IEEE.
Lixi Zhou, Qi Lin, et al.
EDBT 2024
Bharat Sukhwani, Hong Min, et al.
PACT 2012
Bharat Sukhwani, Mathew Thoennes, et al.
SBAC-PAD 2013
Lei Yu, Qi Zhang, et al.
CogMI 2019