ZigZag - Deep Learning Hardware Design Space Exploration
This repository presents the novel version of our tried-and-tested hardware Architecture-Mapping Design Space Exploration (DSE) Framework for Deep Learning (DL) accelerators. ZigZag bridges the gap between algorithmic DL decisions and their acceleration cost on specialized accelerators through a fast and accurate hardware cost estimation.
Functions
zigzag.visualization.results.print_mapping Namespace Reference

Functions

def print_mapping (CostModelEvaluationABC cme, int offsets=2)
 Prints a structured representation of a CostModelEvaluation mapping. More...
 

Function Documentation

◆ print_mapping()

def zigzag.visualization.results.print_mapping.print_mapping ( CostModelEvaluationABC  cme,
int   offsets = 2 
)

Prints a structured representation of a CostModelEvaluation mapping.

:param cme: The CostModelEvaluation to print the mapping of. :param offsets: The number of spaces to offset nested loops.

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