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.
CumulativeCME Class Reference

Represents the sum of multiple CMEs. More...

Inheritance diagram for CumulativeCME:
Collaboration diagram for CumulativeCME:

Public Member Functions

def __init__ (self)
 
def __str__ (self)
 
- Public Member Functions inherited from CostModelEvaluationABC
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...
 

Public Attributes

 accelerator
 

Detailed Description

Represents the sum of multiple CMEs.

This class only contains attributes that make sense for cumulated CMEs

Constructor & Destructor Documentation

◆ __init__()

def __init__ (   self)

Reimplemented from CostModelEvaluationABC.

Member Function Documentation

◆ __str__()

def __str__ (   self)

Member Data Documentation

◆ accelerator

accelerator

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