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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Public Member Functions | |
def | __init__ (self, dict[LayerDim, tuple[int, int]] data) |
tuple[int, int] | __getitem__ (self, LayerDim key) |
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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 | |
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data | |
Static Public Attributes | |
tuple | DEFAULT = (0, 0) |
def __init__ | ( | self, | |
dict[LayerDim, tuple[int, int]] | data | ||
) |
tuple[int, int] __getitem__ | ( | self, | |
LayerDim | key | ||
) |
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data |
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static |