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 LayerTemporalOrdering, including all inherited members.
__contains__(self, Any key) | LayerAttribute | |
__eq__(self, object other) | LayerAttribute | |
__getitem__(self, Any key) | LayerAttribute | |
__hash__(self) | LayerTemporalOrdering | |
__init__(self, list[list[str|UnrollFactorInt]] data) | LayerTemporalOrdering | |
zigzag::workload::layer_attribute::LayerAttribute.__init__(self, Any data) | LayerAttribute | |
__iter__(self) | LayerAttribute | |
__jsonrepr__(self) | LayerAttribute | |
__len__(self) | LayerAttribute | |
__repr__(self) | LayerAttribute | |
__str__(self) | LayerAttribute | |
data | LayerTemporalOrdering | |
empty() | LayerTemporalOrdering | static |
get_constraints(self) | LayerTemporalOrdering | |
is_complete(self, dict[LayerDim, UnrollFactor] temporal_loop_sizes) | LayerTemporalOrdering | |
is_empty(self) | LayerTemporalOrdering | |
remove_invalid_layer_dims(self, LayerDimSizes layer_dim_sizes, str layer_name="") | LayerTemporalOrdering | |
to_legacy_format(self) | LayerTemporalOrdering |