OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
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Taylor Yeonbok Lee ad6a0e7e6d
[IE CLDNN] Add additional cost metric for deciding deconv kernel (#5252)
- gen9_common_conv_bwd_data_kernel (used for fsv16 format) has additional overhead w.r.t stride. Current intention of the kernel implementation is to reuse kernel b/w multiple input values but if the stride gets bigger the reusable rate descreses.
- Thus, added additional panelty for deciding deconv kernel (format)
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inference-engine [IE CLDNN] Add additional cost metric for deciding deconv kernel (#5252) 2021-04-21 17:08:25 +03:00
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OpenVINO™ Toolkit

Stable release Apache License Version 2.0 GitHub branch checks state Azure DevOps builds (branch)

This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.

This open source version includes several components: namely Model Optimizer, nGraph and Inference Engine, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as Caffe*, TensorFlow*, MXNet* and ONNX*.

Repository components:

License

Deep Learning Deployment Toolkit is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.

Resources:

Support

Please report questions, issues and suggestions using:


* Other names and brands may be claimed as the property of others.