FIFO depth optimizer for Vitis backend

[steltze]

FIFO depth optimizer for the Vitis backend using the built-in cosim FIFO profiling feature of Vitis HLS

• Inserted the new optimizer to the Vitis backend flows
• Implemented the optimizer. The steps are similar to the Vivado optimizer, only the profiling and FIFO depth parsing parts change
• Updated the Vitis Writer to overwrite the project.tcl file with Vitis attributes
• Updated the build_prj.tcl file to skip the add_vcd_instructions_tcl function in the case of Vitis backend
• Added a pytest that checks if the depths decreased after the optimization and the cosim with the optimized depths passes, without detecting any deadlocks

Type of change

For a new feature or function, please create an issue first to discuss it
with us before submitting a pull request.

Note: Please delete options that are not relevant.

• Documentation update
• New feature (non-breaking change which adds functionality)

Tests

• Used Vitis HLS 2023.2
• Dummy dense model example. However, optimized depths are the same as the initial ones set by hls4ml

import hls4ml
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Dense(64, input_shape=(16,), name='fc1', activation='relu'))
model.add(Dense(32, name='fc2', activation='relu'))
model.add(Dense(32, name='fc3', activation='relu'))
model.add(Dense(5, name='fc3', activation='softmax'))
model.add(Dense(5, name='fc4', activation='softmax'))

config = hls4ml.utils.config_from_keras_model(model, default_precision='ap_fixed<8, 4>')
config['Flows'] = ['vitis:fifo_depth_optimization']
hls4ml.model.optimizer.get_optimizer('vitis:fifo_depth_optimization').configure(profiling_fifo_depth=200_000)

output_dir = 'hls4mlprj_fifo_depth_opt_vitis'
hls_model = hls4ml.converters.convert_from_keras_model(model,
                                                       io_type='io_stream',
                                                       hls_config=config,
                                                       output_dir=output_dir,
                                                       part='xc7z020clg400-1',
                                                       backend='Vitis')

hls_model.build(reset=False, csim=False, synth=True, cosim=True)
hls4ml.report.read_vivado_report(output_dir)

• Tested the model above with Vivado backend to ensure nothing broke
• 10k param U-Net model. After running cosim with the optimized depths, no deadlocks are detected by Vitis

Test Configuration:

Checklist

• I have read the guidelines for contributing.
• I have commented my code, particularly in hard-to-understand areas.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have installed and run pre-commit on the files I edited or added.
• I have added tests that prove my fix is effective or that my feature works.

[steltze]

hey @vloncar @jmitrevs do you think a unit test is required? To avoid delaying the CI pipeline, I could add a dummy model that verifies synthesis and that the flow is executed. Otherwise, I can add a complete unit test that also checks the FIFO depth results

[vloncar]

I think it would be good to have a full test, but until we improve the synthesis testing pipeline, maybe mark it with @pytest.mark.skip(reason='Skipping synthesis tests for now'). This way we could run it locally when we review your code and it won't cause issues in the CI.

[steltze]

To the reviewers: when we agree on how we want to test this, I can open an issue to also include the Vivado/VivadoAccelerator FIFO optimization in the tests, as a future PR, as some changes in the initial implementation of those backends will be needed

[steltze]

After discussing, some more todos:

• Update the hls4ml documentation by including an example of this feature with the Vitis backend
• Include a UNet model on the pytest
• There was a discussion about using Automated FIFO Sizing instead of Global FIFO Sizing source. @jmitrevs