Internals of svm_train¶
Overview¶
SVM (Support Vector Machine) is a model to predict sample’s classification.
This document describes the structure and execution of svm train, implemented as SVM function.
Basic algorithm¶
Svm_train is a function to trains a batch of samples using support vector machine model and output a weight vecotor for classification. This function used SGD(Stochastic Gradient Descent) method to get convergence.
Implementation¶
The structure of SVM is described as below. It has a svm_dataflow region and control logic region.
There is a iteration loop out of these two regions mentioned above. Its break condition is determined by control logic and maximum iteration config read from DDR.
Config description¶
The sample and config input share one 512-bit port. config is stored in first 512bit. The weight vector is output by a 512-bit port, which will be aligned to 512bit boundaries. Config’s details are documented in the following table:
| 511-448 | 447-384 | 383-320 | 319-256 | 255-192 | 191-128 | 127-64 | 63-0 |
|---|---|---|---|---|---|---|---|
| columns | offset | tolerence | reg_para | step_size | sample_number | max_iteration | features_number |
Resource Utilization¶
The hardware resource utilization of svm_train(4 kernels in 4 SLRs) is shown in the table below (work as 276MHz).
| LUT | LUT as memory | LUT as logic | Register | BRAM36 | URAM | DSP |
| 617382 | 60868 | 556514 | 853134 | 1007 | 32 | 1256 |
Benchmark Result on Board¶
Meanwhile, benchmark results at 276MHz frequency on Alveo U250 board with 2019.2 shell are shown as below:
| Dataset | samples | classes | features | iterations | Spark (4 threads) | Spark (8 threads) | Spark (16 threads) | Spark (32 threads) | Spark (56 threads) | FPGA (:ms) |
| PUF | 2000000 | 2 | 64 | 20 | 192548 (61.9X) | 112903 (36.1X) | 69806 (22.2X) | 68548 (21.9X) | 68080 (21.9X) | 3078 |
| HIGGS | 5000000 | 2 | 28 | 100 | 2224074 (147.6X) | 1401467 (92.9X) | 873412 (57.9X) | 601885 (39.8X) | 590843 (39.1X) | 15067 |