Documentation | FAQ | Release Notes | MiAI Model Zoo | Demo
MiAI Compute Engine is a deep learning inference framework optimized for mobile heterogeneous computing platforms. The design is focused on the following targets:
- Performance
- The runtime is highly optimized with NEON, OpenCL and Hexagon. Except for the inference speed, the initialization speed is also intensively optimized.
- Power consumption
- Chip dependent power options are included as advanced API.
- Memory usage and library footprint
- Graph level memory allocation optimization and buffer reuse is supported.
- Model protection
- Model protection is one the highest priority feature from the beginning of the design. Various techniques are introduced like coverting models to C++ code and literal obfuscations.
- Platform coverage
- A good coverage of recent Qualcomm, MediaTek, Pinecone and other ARM based chips. CPU runtime is also compitable with most POSIX systems and archetectures with limited performance.
MiAI Compute Engine Model Zoo contains several common neural networks models and built daily against a list of mobile phones. The benchmark result can be found in the CI result page.
- GitHub issues: bug reports, usage issues, feature requests
- Gitter:
- QQ群: 756046893
Any kind of contributions are welcome. For bug reports, feature requests, please just open an issue without any hesitance. For code contributions, it's strongly suggested to open an issue for discussion first. For more details, please refer to the contribution guide.
MiAI Compute Engine depends on several open source projects located in third_party directory. Particularly, we learned a lot from the following projects during the development:
- Qualcomm Hexagon NN Offload Framework: the Hexagon DSP runtime depends on this library.
- TensorFlow, Caffe, SNPE, ARM ComputeLibrary, ncnn and many others: we learned many best practices from these projects.
Finally, we also thank the Qualcomm, Pinecone and MediaTek engineering teams for their helps.