DeepC: Deep Neural Network Compiler
dnn Compiler is designed to enable and perform deep learning neural networks by focussing on features of custom ai-accelerators like FPGAs, eFPGAs and other embedded devices like raspberry-pi, odroid, arduino, SparkFun Edge, risc-V and others.
Main component of dnn Compiler has been designed to represent and optimize the common deep learning networks in high level graph IR and to transform the computation graph to minimize memory utilization, optimize data layout and fuse computation patterns for different hardware backends.
Read more at high level design document
build and install dnn Compiler locally from source code with following steps
⭕ Ubuntu 18.04
Follow the steps to install pre-requisites
sudo apt-get update sudo apt-get install build-essential python3.6-dev python3-pip swig doxygen clang-format clang clang-8 llvm-8 llvm-8-dev sudo pip3 install numpy onnx # TODO: libprotobuf-dev protobuf-compiler cmake graphviz libpng-dev wget opencl-headers libgoogle-glog-dev
Once you are done, build dnnCompiler
git clone https://github.com/ai-techsystems/dnnCompiler.git cd dnnCompiler make
find include src swig -name \*.h -print0 -o -name \*.cpp -print0 | xargs -0 -P8 -n1 clang-format -i make -C src make: Entering directory 'dnnCompiler/src' make -C core make: Entering directory 'dnnCompiler/src/core' compiling broadcast.cpp /usr/bin/g++ -O3 -Wall -std=c++14 -fPIC -march=native -msse2 \ -isystem ./packages/eigen-eigen-323c052e1731 -I./include \ -c broadcast.cpp -o obj/broadcast.o compiling tensor.cpp ... ... /usr/bin/g++ -shared ./obj/dnnc_swig.o ./obj/dnnc_pyutils.o ./obj/dnnc_api.o -o lib/libdnnc.so ln -s -f lib/libdnnc.so _dnnc.so /usr/bin/python3 ../test/swig/basic.py
🏃♂️ Using DNNC
We're in pre-alpha stage. However, you can
We'll soon release examples dir.
dnn Compiler adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Checkout the Contributor Guide.
🕵️♂️ Why compiler❔
dnnCompiler is targeted towards devices with small formfactor like microcontrollers, which are part of all sorts of household devices: think appliances, cars, and toys. In fact, there are around 30 billion microcontroller-powered devices produced each year. They're cheap, require very little energy, and are very reliable.
By bringing deep learning models to tiny microcontrollers, we can boost the intelligence of billions of devices that we use in our lives, without relying on expensive hardware or reliable internet connections. Imagine smart appliances that can adapt to your daily routine, intelligent industrial sensors that understand the difference between problems and normal operation, and magical toys that can help kids learn in fun and delightful ways.
🚧 Project Under Development. Stay tuned. We plan to release the first version in Nov. 2019.
This project exists thanks to all the people who contribute. [Contribute].
Become a financial contributor and help us sustain our community. [Contribute]
Support this project with your organization. Your logo will show up here with a link to your website. [Contribute]
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size deepC-0.11-py3-none-any.whl (2.7 MB)||File type Wheel||Python version py3||Upload date||Hashes View hashes|