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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This is the complete list of members for LayerDimSizes, including all inherited members.
__add__(self, "LayerDimSizes" other) | LayerDimSizes | |
__contains__(self, Any key) | LayerAttribute | |
__delitem__(self, LayerDim key) | LayerDimSizes | |
__eq__(self, object other) | LayerAttribute | |
__getitem__(self, Any key) | LayerAttribute | |
__hash__(self) | LayerAttribute | |
__init__(self, dict[LayerDim, UnrollFactor] data) | LayerDimSizes | |
zigzag::workload::layer_attribute::LayerAttribute.__init__(self, Any data) | LayerAttribute | |
__iter__(self) | LayerAttribute | |
__jsonrepr__(self) | LayerAttribute | |
__len__(self) | LayerAttribute | |
__repr__(self) | LayerAttribute | |
__setitem__(self, LayerDim key, UnrollFactor value) | LayerDimSizes | |
__str__(self) | LayerAttribute | |
copy(self) | LayerDimSizes | |
data | LayerDimSizes | |
items(self) | LayerDimSizes | |
layer_dims(self) | LayerDimSizes | |
total_size(self) | LayerDimSizes |