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.
CostModelStage Member List

This is the complete list of members for CostModelStage, including all inherited members.

__init__(self, list[StageCallable] list_of_callables, *Accelerator accelerator, LayerNode layer, SpatialMappingInternal spatial_mapping, SpatialMappingInternal spatial_mapping_int, TemporalMapping temporal_mapping, bool access_same_data_considered_as_no_access=True, **Any kwargs)CostModelStage
zigzag::stages::stage::Stage.__init__(self, list["StageCallable"] list_of_callables, **Any kwargs)Stage
__iter__(self)Stage
acceleratorCostModelStage
access_same_data_considered_as_no_accessCostModelStage
is_leaf(self)CostModelStage
kwargsStage
layerCostModelStage
list_of_callablesStage
run(self)CostModelStage
spatial_mappingCostModelStage
spatial_mapping_intCostModelStage
temporal_mappingCostModelStage