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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Contains the bit precision of each layer operand. More...
Public Member Functions | |
def | __init__ (self, dict[LayerOperand, int] data) |
def | __getitem__ (self, LayerOperand layer_op) |
int | final_output_precision (self) |
Return the precision of either the final output (if defined by user) or the intermediate output. More... | |
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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) |
Public Attributes | |
data | |
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data | |
Contains the bit precision of each layer operand.
def __init__ | ( | self, | |
dict[LayerOperand, int] | data | ||
) |
def __getitem__ | ( | self, | |
LayerOperand | layer_op | ||
) |
int final_output_precision | ( | self | ) |
Return the precision of either the final output (if defined by user) or the intermediate output.
data |