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README.md

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This is a fork of the original library to be able to be run on Hikey 970 board using a makefile on top of scons.

Instructions

  • Run make release for compiling a release build. make debug makes a debug build and make all builds both.
  • After that export LD_LIBRARY_PATH=./build/release to set library path for release build, similarly for debug build.
  • For running specific examples ./build/release/graph_alexnet <parameters>

Utilizing multiple cores

Target Command
Small Cores taskset -c 0-3 ./graph_alexnet --threads=4 --target=NEON
Big Cores taskset -c 4-7 ./graph_alexnet --threads=4 --target=NEON
All Cores taskset -c 0-7 ./graph_alexnet --threads=8 --target=NEON
GPU taskset -c 0-3 ./graph_alexnet--target=CL

CPU+GPU Utilization

There was a python wrapper (the last version of file before being removed) for utilizing both CPU and GPU but it has been superseeded by modifications to individual graphs named graph_alexnet_2, graph_googlenet_2, graph_mobilenet_2, graph_resnet50_2, graph_squeezenet_2. They can be run using: ./graph_alexnet_2 [--cpu] [--gpu] [--n=N] [--i=I] where --cpu and -gpu selects target combinations and n are the total images, i the total inferences per image.

Performance Modeling

This is done via perf.py and results in perf_results folder. Results can be found in perf_cache.dat. It runs performance modeling for graphs and targets with n and i values looped. It uses the curve fitting function and caches in perf_cache.dat and finally plotting in perf_results.

Temperature Modeling

This is done via temp.py and results in temp_results folder. Results can be found in temp_results.log. Similar to performance modeling it loops over graphs and targets and uses curve fitting function to plot a fit along with the threshold temperature 65000. Finally resultant plots are plotted in temp_plots. temp_results.log was manually created by piping the results of temp.py