ONNC 1.0.0 Release
Release Note
New Features
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NVDLA Backend
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The first open-source compiler backend that supports NVIDIA Deep Learning Accelerator (NVDLA)
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Initial release of nv_full hardwre configuration support
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Support status for the models in the ONNX model zoo - ONNC can compile 6 models and run on NVDLA virtual platform successfully. 2 models are not supported by nv_full configuration. The other 4 models need support for more operators.
Models Official NVDLA compiler ONNC compiler bvlc_alexnet O O bvlc_googlenet X O bvlc_reference_caffenet O O bvlc_reference_rcnn_ilsvrc13 O O densenet121 X Need more operator support inception_v1 X O inception_v2 X Need more operator support resnet50 O O shufflenet X Need more operator support squeezenet X Need more operator support vgg19 X nv_full can not support this model zfnet512 X nv_full can not support this model
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Framework Support
- Interpreter Interface - Target backend now can write a customized interpreter.
- Vanilla Backend - A template for porting a new backend
- Statistic API
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Tools
- ONNI
- Add more verbose level for debugging or benchmarking (level 1 to 4)
- Add flag
--dry-run: Do not do the inference, just print statistics. - Add flag
--onnx-opt: Enable onnx optimizer. - Add flag
-fLinearScanAlgo=<string>: Select linear scan algorithm: first-fit, best-fit. (default is first-fit) - Add flag
--enable-x86-fuse-conv-relu: Enable x86 fuse conv relu.
- ONNI
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Documentation
- ONNC-Utilities.md - How to build and run onnc