Comments (1)
At the moment I do not think this is possible, because it is the way XNNPACK behaves. However, I just saw this blog post, which indicates that XNNPACK detailed profiling is available in the next release of TensorFlow. Since the Larq Compute Engine is still based on TF 2.9.0, this is not yet supported. We plan to update LCE to TF 2.10.0 within the coming weeks, so then this should be included as well.
The alternative is to disable XNNPACK (see the command-line options for lce_benchmark_model_aarch64 ), but of course that will give different profiling results. However, it might give you a rough indication of how much time is spent in each layer.
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Related Issues (20)
- Automatic release builds for benchmarking binaries are broken HOT 2
- Deployment on Cortex-M HOT 2
- Tensor transform triggers dequantization HOT 6
- Error on import HOT 2
- Select indirect BGEMM kernels - Benchmarking grouped binary convolutions HOT 3
- LCEInterpreter and converter design HOT 1
- core dumped when number of threads is larger than 2 HOT 3
- Benchmarking custom model HOT 3
- Int8 quantization for microcontroller HOT 13
- Failed import 'org.tensorflow.lite.DataType' on Android project HOT 8
- `convert_keras_model()` does not work as expected for BinaryDenseNet37 Dilated and XNORNet HOT 1
- DoReFa quantizer with higher number of MACs/Ops, Grouped convs as custom ops on LCE 0.7.0 HOT 3
- Error while performing benchmarking HOT 44
- Bool input tensor HOT 7
- extra model size induced by non-parameter layer HOT 1
- Fix Android benchmarker build
- Larq Compute Engine seems incompatible with tensorflow-lite-task-vision on Android (using the latest tensorflow lite demo code) HOT 2
- Dorefa model size and behavior with full precision model and ste_sign model HOT 13
- Cannot save compressed binary or ternary weights, saved as float32 parameters HOT 12
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