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.
LayerPadding Class Reference
Inheritance diagram for LayerPadding:
Collaboration diagram for LayerPadding:

Public Member Functions

def __init__ (self, dict[LayerDim, tuple[int, int]] data)
 
tuple[int, int] __getitem__ (self, LayerDim key)
 
- Public Member Functions inherited from LayerAttribute
def __init__ (self, Any data)
 
int __len__ (self)
 
Iterator[Any] __iter__ (self)
 
def __getitem__ (self, Any key)
 
bool __contains__ (self, Any key)
 
def __str__ (self)
 
def __repr__ (self)
 
Any __jsonrepr__ (self)
 
def __eq__ (self, object other)
 
def __hash__ (self)
 

Static Public Member Functions

def empty ()
 

Public Attributes

 data
 
- Public Attributes inherited from LayerAttribute
 data
 

Static Public Attributes

tuple DEFAULT = (0, 0)
 

Constructor & Destructor Documentation

◆ __init__()

def __init__ (   self,
dict[LayerDim, tuple[int, int]]  data 
)

Member Function Documentation

◆ __getitem__()

tuple[int, int] __getitem__ (   self,
LayerDim  key 
)

◆ empty()

def empty ( )
static

Member Data Documentation

◆ data

data

◆ DEFAULT

tuple DEFAULT = (0, 0)
static

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