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.
MappingValidator Class Reference

Class to validate user-given mappings from yaml file. More...

Public Member Functions

def __init__ (self, Any data)
 Initialize Validator object, assign schema and store normalize user-given data. More...
 
def normalized_data (self)
 Return normalized, user-provided data. More...
 
def invalidate (self, str extra_msg)
 
bool validate (self)
 Validate the user-provided accelerator data. More...
 
None validate_single_mapping (self, dict[str, Any] layer_data)
 

Public Attributes

 validator
 
 schema
 
 is_valid
 

Static Public Attributes

dictionary SCHEMA_SINGLE
 

Detailed Description

Class to validate user-given mappings from yaml file.

Constructor & Destructor Documentation

◆ __init__()

def __init__ (   self,
Any  data 
)

Initialize Validator object, assign schema and store normalize user-given data.

Member Function Documentation

◆ invalidate()

def invalidate (   self,
str  extra_msg 
)
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◆ normalized_data()

def normalized_data (   self)

Return normalized, user-provided data.

◆ validate()

bool validate (   self)

Validate the user-provided accelerator data.

Log a critical warning when invalid data is encountered and return true iff valid.

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◆ validate_single_mapping()

None validate_single_mapping (   self,
dict[str, Any]  layer_data 
)

TODO check that there are no OADimensions that are not defined in the architecture

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Member Data Documentation

◆ is_valid

is_valid

◆ schema

schema

◆ SCHEMA_SINGLE

dictionary SCHEMA_SINGLE
static

◆ validator

validator

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