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