MLAS: improve quantized depthwise convolution#6513
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yufenglee
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Feb 1, 2021
| output_start, | ||
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| padding_data.data()); |
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The input data is continuous. Let's say the size of one image is NSize. Will it be faster to just add NSize on the indirection buffer for each iteration?
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Description: Improve the performance of QLinearConv for depthwise convolutions.
Motivation and Context
On an older Broadwell test system, the mobilenetv2-7.quant.onnx from the E2E example drops from 4.6ms to 3.6ms per inference. The original FP32 model runs in 3.2ms on the same system.