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eba158fb0c
Optimize int8 layers in DNN modules by using RISC-V Vector intrinsic. #25230 This patch optimize 3 functions in the int8 layer by using RVV Native Intrinsic. This patch was tested on QEMU using VLEN=128 and VLEN=256 on `./bin/opencv_test_dnn --gtest_filter="*Int8*"`; On the real device (k230, VLEN=128), `EfficientDet_int8` in `opencv_perf_dnn` showed a performance improvement of 1.46x. | Name of Test | Original | optimized | Speed-up | | ------------------------------------------ | -------- | ---------- | -------- | | EfficientDet_int8::DNNTestNetwork::OCV/CPU | 2843.467 | 1947.013 | 1.46 | ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [ ] I agree to contribute to the project under Apache 2 License. - [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [ ] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake |
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| .. | ||
| batch_norm_layer.cpp | ||
| convolution_layer.cpp | ||
| elementwise_layers.cpp | ||
| eltwise_layer.cpp | ||
| fully_connected_layer.cpp | ||
| layers_common.hpp | ||
| layers_common.simd.hpp | ||
| layers_rvp052.cpp | ||
| layers_rvp052.hpp | ||
| pooling_layer.cpp | ||
| quantization_utils.cpp | ||
| scale_layer.cpp | ||
| softmax_layer.cpp | ||