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Python package / GNU Linux terminal utility for porting machine learning algorithms to FPGA.

Project description


Training opportunities:

1) Training tasks executes in parallel processes.

2) Each training task generates: ".sv" file for FPGA, ".wd" file
and ".tar" archive with model data for prospective
classification task.

Training specialities:

1) "training_file.csv" must contain classes in the first column.

2) "training_file.csv" must not contain a column head.

3) The character - separator of "training_file.csv" must be ",".

4) You can check available training algorithms with option "--info".

Classification opportunities:

1) Classification tasks might be executed parallel on several FPGAs.

2) Several classification tasks, addressed to FPGA, execute consistently.

3) Each classification task generates ".csv" file with predicted answers.

Classification specialities:

1) "classification_file.csv" must not contain a column head.

2) The character - separator of "training_file.csv" must be ",".

3) The "word_dict.wd" might match to firmware of FPGA.

4) You can check available training algorithms and USB serial ports
with option "--info".


1) This example starts the training task:

$ fpga4p --trn "training.csv,train_nbc"

2) This example starts the classification task:

$ fpga4p --cls "class.csv,classify_nbc,training.wd,/dev/ttyUSB0"

3) This example prints available modules for training and
classification, as well as available serial USB ports:

$ fpga4p --info

Project details

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Files for fpga4p, version 1.1
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Filename, size fpga4p-1.1-py3-none-any.whl (19.2 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size fpga4p-1.1.tar.gz (14.5 kB) File type Source Python version None Upload date Hashes View

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