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