Name
Title | ||
|---|---|---|
FPGA-Based QBoost with Large-Scale Annealing Processor and Accelerated Hyperparameter Search |
Abstract | ||
|---|---|---|
QBoost is a recently proposed machine learning algorithm, designed to exploit the benefits of emerging annealing processors which solve NP hard problems in combinatorial optimization a hundred times faster than conventional CPUs. In this paper, we present the first FPGA-based implementation of QBoost, incorporating a large-scale annealing processor with 2704 spins. In contrast to previous implementations, based on quantum annealers, we utilize the flexibility of FPGAs for implementing a fast, integrated QBoost engine which combines the annealing processor and the modules of the hyperparameter search on a single FPGA. As opposed to quantum annealers, this accelerates the time required for scanning the hyperparameter space from the order of hours to a single second. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/RECONFIG.2018.8641713 | 2018 International Conference on ReConFigurable Computing and FPGAs (ReConFig) |
Keywords | Field | DocType |
QBoost,Annealing Processor,Supervised Machine Learning,Binary classification,Ensemble Learning | Hyperparameter,Binary classification,Computer science,Parallel computing,Field-programmable gate array,Exploit,Combinatorial optimization,Annealing (metallurgy),Ensemble learning | Conference |
ISSN | ISBN | Citations |
2325-6532 | 978-1-7281-1968-7 | 0 |
PageRank | References | Authors |
0.34 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takashi Takemoto | 1 | 0 | 0.34 |
| Normann Mertig | 2 | 3 | 1.63 |
| Masato Hayashi | 3 | 6 | 2.32 |
| Saki Susa-Tanaka | 4 | 0 | 0.34 |
| Hiroshi Teramoto | 5 | 3 | 2.64 |
| Atsuyoshi Nakamura | 6 | 306 | 51.88 |
| Ichigaku Takigawa | 7 | 209 | 18.15 |
| Shin-ichi Minato | 8 | 725 | 84.72 |
| Tamiki Komatsuzaki | 9 | 0 | 2.37 |
| Masanao Yamaoka | 10 | 128 | 23.39 |