FPGA-Based Acceleration Techniques for High-Throughput Data Processing
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V2I3P102Keywords:
FPGAs, Inline acceleration, Data processing, High-throughput, Real-time analytics, Parallel processingAbstract
Accelerating data processing is crucial across various industries. Field-Programmable Gate Arrays (FPGAs) have emerged as effective tools for speeding up data-intensive processes due to their reconfigurability and parallel processing capabilities. FPGA inline accelerators offer a hardware parallel platform that efficiently handles real-time data analytics workloads. They ingest data while performing inline processing for data conditioning, providing real-time insights from streaming data. By placing the analytic pipeline close to the point of ingress and leveraging hardware acceleration for initial data analysis, FPGAs mitigate latency issues associated with traditional data analytics technologies. FPGAs' inherent architecture allows for massively parallel, real-time processing, making them suitable for early-stage processing tasks such as initial filtering, transformations, data cleaning, and enrichments. FPGA inline accelerators reside between the network interface card (NIC) and the CPU, intercepting incoming packets and enabling functions like transformations, pattern detectors, filters, and compression/decompression in real time. This approach provides invaluable insights in data analytics and reduces data volumes before they pass through software stacks, offloading the CPU. Furthermore, FPGA-based accelerators offer a deterministic approach regardless of data rate or formats, simplifying the system by eliminating the need for complex flow control and load balance management
References
[1] Intel. FPGA inline acceleration for streaming analytics (White paper). https://cdrdv2-public.intel.com/650517/wp-01278-fpga-inline-acceleration-for-streaming-analytics.pdf
[2] IEEE. (2020). The role of FPGAs in data center acceleration: Unleashing power and efficiency. IEEE Xplore. https://ieeexplore.ieee.org/document/9439431/
[3] MDPI. (2019). High-performance FPGA acceleration for image processing applications. Journal of Imaging, 5(1), 16. https://www.mdpi.com/2313-433X/5/1/16
[4] NVIDIA. FPGA acceleration for AI and deep learning: Performance improvements and challenges. https://www.fpgainsights.com/fpga/the-role-of-fpgas-in-data-center-acceleration-unleashing-power-and-efficiency/
[5] TUDelft. (2020). A special issue on FPGA applications in computational architectures. https://pure.tudelft.nl/ws/portalfiles/portal/104878264/2020_IEEE_CAS_M_Special_Issue_on_FPGAs_1_.pdf
[6] VLDB. (2020). Optimizing FPGA acceleration in database systems. Proceedings of the VLDB Endowment, 13(1), 71–82. http://www.vldb.org/pvldb/vol13/p71-owaida.pdf
[7] VLSI First. Accelerating data processing with FPGA design: Applications and benefits. https://vlsifirst.com/blog/accelerating-data-processing-with-fpga-design-applications-and-benefits
[8] Xilinx. Challenges and opportunities in FPGA acceleration for big data analytics. ResearchGate. https://www.researchgate.net/publication/364082834_FPGA_Acceleration_for_Big_Data_Analytics_Challenges_and_Opportunities
