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.
LomaEngine Member List

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

__init__(self, *Accelerator accelerator, LayerNode layer, SpatialMappingInternal spatial_mapping, TemporalMappingType mapping_type, int|None loma_lpf_limit=None, **Any kwargs)LomaEngine
acceleratorLomaEngine
compute_nb_permutations(self)LomaEngine
constraintsLomaEngine
find_smallest_non_static_pf(self, LayerDim layer_dim)LomaEngine
get_prime_factors(self)LomaEngine
get_temporal_loops(self)LomaEngine
has_constraintsLomaEngine
layerLomaEngine
limit_lpfs(self)LomaEngine
lpf_limitLomaEngine
lpfsLomaEngine
mapping_typeLomaEngine
memory_hierarchyLomaEngine
nb_permutationsLomaEngine
ordering_generator(self)LomaEngine
reduce_static_fps(self)LomaEngine
run(self)LomaEngine
set_constraints(self, list[PermutationConstraint] constraints)LomaEngine
show_progress_barLomaEngine
spatial_mappingLomaEngine
temporal_loop_dim_sizeLomaEngine
temporal_loop_pf_count_sumsLomaEngine
temporal_loop_pf_countsLomaEngine
update_min_lpf_factor(self, dict[LayerDim, UnrollFactor] loop_sizes)LomaEngine