Support Vector Machine¶

Support vector machine(svm) resides in L2/benchmarks/classification/svm directory.

Dataset¶

There are 2 dataset used in the benchmark:

1 - PUF (https://archive.ics.uci.edu/ml/datasets/Physical+Unclonable+Functions)

2 - HIGGS (https://archive.ics.uci.edu/ml/datasets/HIGGS)

Dataset	samples	features	iterations
1	2000000	64	20
2	5000000	28	100

Executable Usage¶

Work Directory(Step 1)

The steps for library download and environment setup can be found in Vitis Data Analytics Library. For getting the design,

cd L2/benchmarks/classification/svm

Build kernel(Step 2)

Run the following make command to build your XCLBIN and host binary targeting a specific device. Please be noticed that this process will take a long time, maybe couple of hours.

make run TARGET=hw DEVICE=xilinx_u250_gen3x16_xdma_3_1_202020_1  HOST_ARCH=x86

Run kernel(Step 3)

To get the benchmark results, please run the following command.

./build_dir.hw.xilinx_u250_gen3x16_xdma_3_1_202020_1/test_svm.exe -xclbin build_dir.hw.xilinx_u250_gen3x16_xdma_3_1_202020_1/svm_4krnl.xclbin -in ./ml_datasets/1000.csv -trn 999 -ten 100 -fn 64 -itrn 1 -bn 10

Support vector machine Input Arguments:

Usage: test_svm.exe -xclbin <xclbin_name> -in <input_data> -trn <TBD> -ten <TBD> -fn <TBD> -itrn <TBD> -bn <TBD >
       -xclbin:      the kernel name
       -in    :      input data
       -trn   :      TBD
       -ten   :      TBD
       -fn    :      TBD
       -itrn  :      TBD
       -bn    :      TBD

Note: Default arguments are set in Makefile, you can use other platforms to build and run.

Example output(Step 4)

--------- SVM Test ---------
Found Platform
Platform Name: Xilinx
Selected Device xilinx_u250_gen3x16_xdma_shell_3_1
INFO: Importing build_dir.hw.xilinx_u250_gen3x16_xdma_3_1_202020_1/svm_4krnl.xclbin
...
rows num:999
cols num:65
creating buf_data
creating buf_weight
DDR buffers have been mapped/copy-and-mapped
Kernel execution timn: 0.23ms
Decision Tree FPGA times:1 ms
kernel0:0: -0.00900901
kernel0:1: -0.0510511
kernel0:2: -0.033033
...
kernel3:62: -0.013013
kernel3:63: -0.047047

------------------------------------------------------------

Profiling¶

The support vector machine design is validated on Alveo U250 board at 300 MHz frequency. The hardware resource utilizations are listed in the following table.

Table 1 Hardware resources for support vector machine¶
Name	LUT	BRAM	URAM	DSP
Platform	178466	403	0	13
SVM	366992	276	132	1232
SVM_1	91725	69	33	308
SVM_2	91671	69	33	308
SVM_3	91815	69	33	308
SVM_4	91781	69	33	308
User Budget	1547750	2285	1280	12275
Used Resources	366992	276	132	1232
Percentage	23.71%	12.08%	10.31%	10.04%

The performance is shown below.: In above test, this design takes 0.23ms to process 999 samples with 65 features, so its throughput is 1.05GB/s.