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.
PickleSaveStage Class Reference

Class that dumps all received CMEs into a list and saves that list to a pickle file. More...

Inheritance diagram for PickleSaveStage:
Collaboration diagram for PickleSaveStage:

Public Member Functions

def __init__ (self, list[StageCallable] list_of_callables, *str pickle_filename, **Any kwargs)
 
def run (self)
 Run the simple save stage by running the substage and saving the CostModelEvaluation simple json representation. More...
 
- Public Member Functions inherited from Stage
def __init__ (self, list["StageCallable"] list_of_callables, **Any kwargs)
 
def __iter__ (self)
 
bool is_leaf (self)
 

Public Attributes

 pickle_filename
 
- Public Attributes inherited from Stage
 kwargs
 
 list_of_callables
 

Detailed Description

Class that dumps all received CMEs into a list and saves that list to a pickle file.

Constructor & Destructor Documentation

◆ __init__()

def __init__ (   self,
list[StageCallable list_of_callables,
*str  pickle_filename,
**Any  kwargs 
)
Parameters
list_of_callablessee Stage
pickle_filenameoutput pickle filename
kwargsany kwargs, passed on to substages

Member Function Documentation

◆ run()

def run (   self)

Run the simple save stage by running the substage and saving the CostModelEvaluation simple json representation.

This should be placed above a ReduceStage such as the SumStage, as we assume the list of CMEs is passed as extra_info

Reimplemented from Stage.

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Member Data Documentation

◆ pickle_filename

pickle_filename

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