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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Abstract Base Class to represent any layer attribute. More...
Public Member Functions | |
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) |
Public Attributes | |
data | |
Abstract Base Class to represent any layer attribute.
def __init__ | ( | self, | |
Any | data | ||
) |
bool __contains__ | ( | self, | |
Any | key | ||
) |
def __eq__ | ( | self, | |
object | other | ||
) |
def __getitem__ | ( | self, | |
Any | key | ||
) |
def __hash__ | ( | self | ) |
Reimplemented in LayerTemporalOrdering, and SpatialMapping.
Iterator[Any] __iter__ | ( | self | ) |
Any __jsonrepr__ | ( | self | ) |
int __len__ | ( | self | ) |
def __repr__ | ( | self | ) |
def __str__ | ( | self | ) |
Reimplemented in MemoryOperandLinks, and SpatialMapping.
data |