deepC

The deepC is a vendor independent deep learning library, compiler and inference framework designed for small form-factor devices including μControllers, IoT and Edge devices

🏃‍♂️ Using deepC

Here are few of many ways.

Try deepC with Colab Noteboook
Install it on Ubuntu, raspbian (or any other debian derivatives) using pip install deepC
Compile onnx model- read this article or watch this video
Use deepC with a Docker File

See more examples in tutorial dir.

📛 what is deepC?

deepC library, compiler and inference framework is designed to enable and perform deep learning neural networks by focussing on features of small form-factor devices like micro-controllers, eFPGAs, cpus and other embedded devices like raspberry-pi, odroid, arduino, SparkFun Edge, risc-V, mobile phones, x86 and arm laptops among others.

edge Devices

deepC also offers ahead of time compiler producing optimized executable based on LLVM compiler tool chain specialized for deep neural networks with ONNX as front end.

📝 Design

Main components of deepC have 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.

Architecture

💧 PreRequisites

💻 Development

Build and start modifying deepC 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 protobuf-compiler libprotoc-dev
sudo pip3 install numpy==1.15.0 onnx==1.5.0

Once you are done, build deepC

git clone https://github.com/ai-techsystems/deepC.git
cd deepC
make

⭕ Mac OS / Windows 10

Make sure you have the below pre-requisites

Mac OS:

Windows 10:

Once you are done, build deepC inside docker container

git clone https://github.com/ai-techsystems/deepC.git
cd deepC
python buildDocker.py

📜 Output

find include src swig -name \*.h -print0 -o -name \*.cpp -print0 | xargs -0 -P8 -n1 clang-format -i
make -C src
make[1]: Entering directory 'deepC/src'
make -C core
make[2]: Entering directory 'deepC/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

Current Support

Supported Architectures	Status
Arm	✔️
Armv7	✔️
Arm64	✔️
AMD64	✔️
ppc64le	✔️

Supported OS	Distributions	Status
Linux	Ubuntu 18.04	✔️
Linux	CentOS 6	✔️
Linux	Arch Linux	✔️
Linux	Manjaro	✔️
Windows	1803 and above	✔️
Mac OS	Sierra and above	✔️

➕ Contribute

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.

🙏 Acknowledgement

We acknowledge the efforts predecessor projects like LLVM, ONNX etc. to make this project a reality.

🕵️‍♂️ Why compiler❔

deepC 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.

Organizations

Support this project with your organization. Your logo will show up here with a link to your website. [Contribute]

Built on/with deepC

Products

No code TinyML platform, built with deepC technology.
No code TinyML Book, with a chapter on deepC.

Papers

Paper: Deep Neural Network Operators, appeared in Proceedings of AITS Summit, 2019
Letter: Gesture Recognition with deepC, appeared in INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS
Poster: Deep Neural Network Compiler and Inference Framework for microcontrollers and microcomputers, appeared in IRISS 2020 14th Inter-Research-Institute Student Seminar in Computer Science

Paper Citations

Book Chapter

deepC Chapter in book Introduction to TinyML, available on Amazon and other retailers