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.
CostModelEvaluationABC Class Reference

Superclass for CostModelEvaluation and CumulativeCME. More...

Inheritance diagram for CostModelEvaluationABC:
Collaboration diagram for CostModelEvaluationABC:

Public Member Functions

None __init__ (self)
 
def core (self)
 
"CumulativeCME" __add__ (self, "CostModelEvaluationABC" other)
 
def __mul__ (self, int number)
 
dict[str, float] __simplejsonrepr__ (self)
 Simple JSON representation used for saving this object to a simple json file. More...
 
def __jsonrepr__ (self)
 JSON representation used for saving this object to a json file. More...
 

Detailed Description

Superclass for CostModelEvaluation and CumulativeCME.

Constructor & Destructor Documentation

◆ __init__()

None __init__ (   self)

Reimplemented in CumulativeCME.

Member Function Documentation

◆ __add__()

"CumulativeCME" __add__ (   self,
"CostModelEvaluationABC"  other 
)

◆ __jsonrepr__()

def __jsonrepr__ (   self)

JSON representation used for saving this object to a json file.

Reimplemented in CostModelEvaluationForIMC.

Here is the call graph for this function:

◆ __mul__()

def __mul__ (   self,
int  number 
)
Here is the call graph for this function:

◆ __simplejsonrepr__()

dict[str, float] __simplejsonrepr__ (   self)

Simple JSON representation used for saving this object to a simple json file.

Reimplemented in CostModelEvaluationForIMC.

◆ core()

def core (   self)

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