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Open Neural Network Compiler
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.github Closes #106 - Added issue templates Oct 25, 2018
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docs doc: Add missing figure in C backend guide May 6, 2020
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tools [onnc] Update compiler version to 1.3.0 May 27, 2020
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.travis.yml Build onnc with automake in Travis Sep 5, 2018
BUGS Add basic project documents. Aug 2, 2018 Release 1.3.0 Apr 29, 2020
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LICENSE.TXT Clarify copyright holder in 3-Clause BSD License Aug 13, 2018 Revert "tests: move all tests from `tools` to `tests`" May 21, 2019 Update Oct 13, 2019 Release 1.3.0 Apr 29, 2020
VERSION Release 1.3.0 Apr 29, 2020 Reorganize directory structure. Jan 23, 2018 Revert "tests: move all tests from `tools` to `tests`" May 21, 2019

ONNC (Open Neural Network Compiler)


ONNC (Open Neural Network Compiler) is a retargetable compilation framework designed specifically for proprietary deep learning accelerators. Its software architecture expedites porting ONNC to any Deep Learning Accelerator (DLA) design that supports ONNX (Open Neural Network Exchange) operators. ONNC guarantees executability across every DLA by means of transforming ONNX models into DLA-specific binary forms and leveraging the intermediate representation (IR) design of ONNX along with effective algorithms to eliminate the overhead of data movement. ONNC is the first open source compiler available for NVDLA-based hardware designs. Its NVDLA backend can compile a model into an executable NVDLA Loadable file. Integrating ONNC with the NVDLA software stack opens up opportunities for developers and researchers to explore the NVDLA-based inference design at system level.


  • W. F. Lin, D. Y. Tsai, L. Tang, C. T. Hsieh, C. Y. Chou, P. H. Chang, and L. Hsu, “ONNC: A compilation framework connecting ONNX to proprietary deep learning accelerators,” in IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2019). IEEE, 2019.

  • W.F. Lin, C. T. Hsieh, C. Y. Chou, "ONNC-based Software Development Platform for Configurable NVDLA Designs", to appear in IEEE International Symposium on VLSI Design, Automation and Test (VLSI-DAT 2019). IEEE, 2019


Current Status

How to contribute

Directory Structure

  • - This document
  • docs - documents
  • include - header files for libonnc
  • lib - implementation for libonnc
  • tools - tools based on libonnc

Supported platforms

ONNC supports Ubuntu/x86_64 and MacOSX.

Here is a list of verified versions:

  • Ubuntu/x86_64

    • 16.04
  • MacOSX

    • High Sierra

Getting Started

There are three ways to build ONNC:

  1. Build ONNC via Docker
    Please refer to the ONNC Utilities document.
  2. Build ONNC via ONNC umbrella
    Please follow the instructions of in onnc-umbrella.
    Here is the version of external library we are using in ONNC.
  3. Build ONNC without ONNC umbrella
    Please refer to the ONNC CMake build instruction
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