Note: The data and scripts here are all stale. Please go to https://github.com/dmlc/tvm/wiki/Benchmark#mobile-gpu For the latest results.
This repo is the supporting material for Optimizing Mobile Deep Learning on ARM GPU with TVM
Tested on
Firefly-RK3399 4G, CPU: dual-core Cortex-A72 + quad-core Cortex-A53, GPU: Mali-T860MP4
Arm Compute Library: v17.12, MXNet: v1.0.1, Openblas: v0.2.18
sudo /etc/init.d/lightdm stop
sudo -i
echo performance > /sys/class/misc/mali0/device/devfreq/ff9a0000.gpu/governor
This can make the environment more stable.
Note: You need more than 2.5GB of memory to run the following test.
Otherwise, you must skip the test of vgg16 by replacing --model all with --model resnet18 or --model mobilenet
in the commond.
In TVM, we use RPC to do test, so you should build TVM runtime and start a RPC server on your device.
python -m tvm.exec.rpc_server --host 0.0.0.0 --port=9090
Then in your host machine, run the test commond
python mali_imagenet_bench.py --target-host TARGET_HOST --host HOST --port PORT --model allReplace the TARGET_HOST, HOST and PORT with the corresponding values in your environment.
For example, on my Firefly-RK3399, the commond is
python mali_imagenet_bench.py --target-host 'llvm -target=aarch64-linux-gnu -mattr=+neon' --host 10.42.0.96 --port 9090 --model allThis test is executed locally on your device. So you need install the mxnet with openblas on your device first.
python mxnet_test.py --model allBuild ACL by cross-compile on host system.
scons Werror=1 neon=1 opencl=1 examples=1 benchmark_tests=1 os=linux arch=arm64-v8a embed_kernels=1 -j$(nproc)copy acl_test.cc to the root directoy of ACL and build the acl_test by
aarch64-linux-gnu-g++ acl_test.cc build/utils/*.o -O2 -std=c++11\
-I. -Iinclude -Lbuild -Lbuild/opencl-1.2-stubs/\
-larm_compute -larm_compute_graph -larm_compute_core -lOpenCL -o acl_testcopy the binary file acl_test to your device and run
./acl_test all
cat result-acl.txt
results are recored in result-acl.txt
Note Some testcases (e.g. resnet) are missing because Arm Compute Library currently (v17.12) does not support skip connection in its graph runtime. Also some testcases are too slow so that be skipped.
Paste the outputs on my board here.
============================================================
model: vgg16, dtype: float32
warm up..
test..
cost per image: 1.2926s
============================================================
model: vgg16, dtype: float16
warm up..
test..
cost per image: 0.6896s
============================================================
model: resnet18, dtype: float32
warm up..
test..
cost per image: 0.2041s
============================================================
model: resnet18, dtype: float16
warm up..
test..
cost per image: 0.1183s
============================================================
model: mobilenet, dtype: float32
warm up..
test..
cost per image: 0.0767s
============================================================
model: mobilenet, dtype: float16
warm up..
test..
cost per image: 0.0479s
============================================================
model: vgg16, dtype: float32
warm up...
test..
cost per image: 3.0250s
============================================================
model: resnet18, dtype: float32
warm up...
test..
cost per image: 0.3977s
============================================================
model: mobilenet, dtype: float32
warm up...
test..
cost per image: 0.2914s
backend: cl model: vgg16 conv_method: gemm dtype: float32 cost: 1.64456
backend: cl model: vgg16 conv_method: gemm dtype: float16 cost: 0.969372
backend: cl model: vgg16 conv_method: direct dtype: float32 cost: 3.90031
backend: cl model: vgg16 conv_method: direct dtype: float16 cost: 1.61179
backend: cl model: mobilenet conv_method: gemm dtype: float32 cost: 0.170934
backend: cl model: mobilenet conv_method: direct dtype: float32 cost: 0.173883
backend: neon model: vgg16 conv_method: gemm dtype: float32 cost: 4.10269