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.
Public Member Functions | List of all members
SumStage Class Reference

Class that keeps yields only the sum of all cost model evaluations generated by its substages created by list_of_callables. More...

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Public Member Functions

def run (self)
 
- Public Member Functions inherited from Stage
def __init__ (self, list["StageCallable"] list_of_callables, **Any kwargs)
 
def __iter__ (self)
 
bool is_leaf (self)
 

Additional Inherited Members

- Public Attributes inherited from Stage
 kwargs
 
 list_of_callables
 

Detailed Description

Class that keeps yields only the sum of all cost model evaluations generated by its substages created by list_of_callables.

Member Function Documentation

◆ run()

def run (   self)

Reimplemented from Stage.

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The documentation for this class was generated from the following file: