Comments (3)
I think we should do TF next. Javier gave me a TF model to try
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A prioritized list:
- Keras/TensorFlow ✅
- PyTorch
- Caffe
- MXNet
Thoughts?
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we have a better idea of what we want to do now
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Related Issues (20)
- SeparableConv1D fail CSynth with Vitis backend
- hls_model.build() cannot not work in Jupyter HOT 4
- ERROR: [XFORM 203-504] Stop unrolling loop 'MultLoop' HOT 1
- ERROR: [XFORM 203-504] Stop unrolling loop 'MultLoop' HOT 8
- Multiple stream clones with different numbers of outputs crash Keras converter.
- hls4ml building project HOT 3
- Inconsistant behaviour of `accum_t` in `io_stream` and `io_parallel` for `pooling` layers with `Vivado` backend
- part7b of tutorial has no result returned back
- [XFORM 203-502] HOT 6
- Non-quantized QKeras layers break conversion
- Concatenation Layer Issue with PyTorch ResNet
- Keras Reshape Layer is Built with Error HOT 1
- vivadoaccelerator backend : bit file note generated HOT 5
- About QBatchNormalization is not support QKeras po2 quantizer HOT 1
- ERROR: [XFORM 203-504] Stop unrolling loop 'Product1' (firmware/nnet_utils/nnet_dense_latency.h:37) in function 'nnet::dense_latency<ap_fixed<16, 6, (ap_q_mode)5, (ap_o_mode)3, 0>, ap_fixed<16, 6, (ap_q_mode)5, (ap_o_mode)3, 0>, config42_mult>' because it may cause large runtime and excessive memory usage due to increase in code size. Please avoid unrolling the loop or form sub-functions for code in the loop body. myproject_prj:solution1 Dec 27, 2023 6:47:26 PM
- Failure at converters.convert_from_pytorch_model . compile/build()
- Move transpose based on backend from hls4ml/model/layers.py to backend-specific areas
- Problem tracing binary CNN model after recent tracing optimization HOT 8
- Wrong prediction of C Simulation compared to QKeras? HOT 3
- The hls4ml transformation successful, however, the result of inference all zeros. HOT 1
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