About
Kernel methods are fundamental in machine learning. They excel in regression, classification, and dimensionality reduction. They model nonlinear relationships and are widely used in many fields such as in face recognition, wind forecasting, and molecular energy estimation. However, their reliance on a kernel matrix leads to quadratic complexity in computation and storage, which makes large-scale datasets challenging. We propose an FPGA-accelerated library with high parallelism and efficient data movement. The library is accessed from Python via PYNQ with a plug-and-play solution without requiring hardware expertise. It currently supports Kernel Ridge Regression and Gaussian Processes. Five kernel functions are supported, with Partial Reconfiguration to switch between them. We achieve 30x speedup compared to Python libraries on a Kria SOM. Future work includes extending support to other kernel methods, and heterogeneous FPGA architectures like AMD Versal, with more advanced kernels..
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The talk
Credits and licence
- Institution
- CERN
- Department
- FPGA Developers' Forum (FDF)
- Speaker
- Yousef Alnaser
- Year
- 2025
- Language
- English
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed