Name
Title | ||
|---|---|---|
High performance lattice regression on FPGAs via a high level hardware description language |
Abstract | ||
|---|---|---|
Lattice regression-based models are highly-constrainable and interpretable machine learning models used in applications such as query classification and path length prediction for maps. To improve their performance and better serve these models to millions of consumers, we accelerate them using field programmable gate arrays. We adopt a library-based approach using a high level hardware descriptio... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICFPT52863.2021.9609893 | 2021 International Conference on Field-Programmable Technology (ICFPT) |
Keywords | DocType | ISBN |
Lattice Regression,Machine Learning,Field Programmable Gate Array (FPGA),Hardware Acceleration | Conference | 978-1-6654-2010-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nathan Zhang | 1 | 0 | 0.34 |
| Matthew Feldman | 2 | 36 | 1.99 |
| Kunle Olukotun | 3 | 4532 | 373.50 |