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 SalsaState, including all inherited members.
__init__(self, Accelerator accelerator, LayerNode layer, SpatialMappingInternal spatial_mapping, list[tuple[LayerDim, UnrollFactorInt]] ordering, str opt_criterion_name, TemporalMappingType mapping_type) | SalsaState | |
accelerator | SalsaState | |
cme | SalsaState | |
layer | SalsaState | |
mapping_type | SalsaState | |
memory_hierarchy | SalsaState | |
opt_criterion | SalsaState | |
opt_criterion_name | SalsaState | |
ordering | SalsaState | |
spatial_mapping | SalsaState | |
swap(self, int i, int j) | SalsaState | |
temporal_mapping | SalsaState |