Kalman Filter

Kalman Filter example resides in L2/examples/kalmanfilter directory.

This benchmark tests the performance of kalmanfilter function. The classic Kalman Filter is proposed for linear system.

The tutorial provides a step-by-step guide that covers commands for building and running kernel.

Executable Usage

• Work Directory(Step 1)

The steps for library download and environment setup can be found in README file of L2 folder. For getting the design,

cd L2/example/kalmanfilter

• 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.

export OPENCV_INCLUDE=< path-to-opencv-include-folder >
export OPENCV_LIB=< path-to-opencv-lib-folder >
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:< path-to-opencv-lib-folder >
export DEVICE=< path-to-platform-directory >/< platform >.xpfm
make host xclbin TARGET=hw

• Run kernel(Step 3)

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

make run TARGET=hw

• Example output(Step 4)

-----------Kalman Design---------------
INFO: Init cv::Mat objects.
INFO: Kalman Filter Verification:
     Number of state variables: 16
     Number of measurements: 16
     Number of control input: 16
INFO: Running OpenCL section.
Found Platform
Platform Name: Xilinx
INFO: Device found - xilinx_u200_xdma_201830_2
XCLBIN File Name: krnl_kalmanfilter
INFO: Importing Vitis_Libraries/vision/L2/examples/kalmanfilter/Xilinx_Kalmanfilter_L2_Test_vitis_hw_u200/build_dir.hw.xilinx_u200_xdma_201830_2/krnl_kalmanfilter.xclbin
Loading: 'Vitis_Libraries/vision/L2/examples/kalmanfilter/Xilinx_Kalmanfilter_L2_Test_vitis_hw_u200/build_dir.hw.xilinx_u200_xdma_201830_2/krnl_kalmanfilter.xclbin'
INFO: Test Pass
------------------------------------------------------------

Profiling

The Kalman Filter design is validated on Alveo u200 board at 300 MHz frequency. The hardware resource utilizations are listed in the following table.

Table 1 Hardware resources for Kalman Filter

Dataset			LUT	BRAM	FF	DSP
Resolution	NPPC	other params
NA	1	SV - 16x16x16	59342	98	90762	361

The performance is shown below

Table 2 Performance numbers for Kalman Filter

Dataset	Latency (CPU)	Latency(FPGA)
SV - 16x16x16	6.75 ms	0.55 ms