elasticai.creator.nn.fixed_point.linear.testbench

Module Contents

Classes

LinearDesignProtocol
LinearTestbench

API

class elasticai.creator.nn.fixed_point.linear.testbench.LinearDesignProtocol

Bases: typing.Protocol

abstract property name: str

abstract property port: elasticai.creator.vhdl.design.ports.Port

abstract property in_feature_num: int

abstract property out_feature_num: int

abstract property frac_width: int

abstract property data_width: int

class elasticai.creator.nn.fixed_point.linear.testbench.LinearTestbench(name: str, uut: elasticai.creator.nn.fixed_point.linear.testbench.LinearDesignProtocol)

Bases: elasticai.creator.vhdl.simulated_layer.Testbench

Initialization

save_to(destination: elasticai.creator.file_generation.savable.Path)

property name: str

prepare_inputs(*inputs) → list[dict]

parse_reported_content(content: list[str]) → list[list[list[float]]]

This function parses the reported content, which is just a list of strings. All lines starting with ‘output_text:’ are considered as a result of the testbench. These results will be stacked for each batch. So you get a list[list[list[float]]] which is similar to batch[out channels[output neurons[float]]]. For linear layer the output neurons is 1. For each item reported it is checked if the string starts with ‘result: ‘. If so the remaining part will be split by ‘,’. The first number gives the batch. The second the result.