提交 8270b151 编写于 作者: Z Zero King

Update README.md

上级 70966273
<div align="center">
<img src="docs/mace-logo.png" width = "400" alt="MACE" />
<div align="center">
<img src="docs/mace-logo.png" width="400" alt="MACE" />
</div>
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introduced to allow better preemption for the UI rendering task.
* Memory usage and library footprint
* Graph level memory allocation optimization and buffer reuse are supported.
The core library tries to keep minium external dependencies to keep the
The core library tries to keep minimum external dependencies to keep the
library footprint small.
* Model protection
* Model protection is the highest priority feature from the beginning of
the design. Various techniques are introduced like coverting models to C++
the design. Various techniques are introduced like converting 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 compatible with most POSIX systems and
archetectures with limited performance.
architectures with limited performance.
## Getting Started
* [Introduction](https://mace.readthedocs.io/en/latest/getting_started/introduction.html)
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## Performance
[MACE Model Zoo](https://github.com/XiaoMi/mace-models) contains
several common neural networks and models which will be built daily against a list of mobile
phones. The benchmark result can be found in the CI result page.
phones. The benchmark results can be found in the CI result page.
## Communication
* GitHub issues: bug reports, usage issues, feature requests
* Mailing list: [mace-users@googlegroups.com](mailto:mace-users@googlegroups.com)
* Google groups: https://groups.google.com/forum/#!forum/mace-users
* Google Groups: https://groups.google.com/forum/#!forum/mace-users
* QQ群: 756046893
## Contributing
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[Apache License 2.0](LICENSE).
## Acknowledgement
MACE depends on several open source projects located in
MACE depends on several open source projects located in the
[third_party](third_party) directory. Particularly, we learned a lot from
the following projects during the development:
* [Qualcomm Hexagon NN Offload Framework](https://source.codeaurora.org/quic/hexagon_nn/nnlib): the Hexagon DSP runtime
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practices from these projects.
Finally, we also thank the Qualcomm, Pinecone and MediaTek engineering teams for
their helps.
their help.
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