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.
SalsaStage Member List

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

__init__(self, list[StageCallable] list_of_callables, *Accelerator accelerator, LayerNode layer, SpatialMappingInternal spatial_mapping, TemporalMappingType temporal_mapping_type, **Any kwargs)SalsaStage
zigzag::stages::stage::Stage.__init__(self, list["StageCallable"] list_of_callables, **Any kwargs)Stage
__iter__(self)Stage
best_cmeSalsaStage
cme_queueSalsaStage
compare_cme_energy(self, CostModelEvaluation cme)SalsaStage
compare_cme_latency(self, CostModelEvaluation cme)SalsaStage
compare_stageSalsaStage
engineSalsaStage
is_leaf(self)Stage
kwargsStage
list_of_callablesStage
mapping_typeSalsaStage
number_of_coreSalsaStage
number_of_core_allocatedSalsaStage
opt_criterion_nameSalsaStage
run(self)SalsaStage
spatial_mappingSalsaStage
worker_listSalsaStage