- Generated by
1.9.1
|
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.
|
Namespaces | |
| zigzag.api | |
Functions | |
| ( tuple[float, float, list[tuple[CostModelEvaluationABC, Any]]]|tuple[float, float, float, float, list[tuple[CostModelEvaluationABC, Any]]]) | get_hardware_performance_zigzag (str|list[dict[str, Any]]|ModelProto workload, str accelerator, str mapping, *Literal["loma"]|Literal["salsa"] temporal_mapping_search_engine="loma", Literal["uneven"]|Literal["even"] temporal_mapping_type="uneven", str opt="latency", str dump_folder=f"outputs/{datetime.now()}", str|None pickle_filename=None, int lpf_limit=6, int nb_spatial_mappings_generated=3, bool in_memory_compute=False, bool exploit_data_locality=False, bool enable_mix_spatial_mapping=False, bool loma_show_progress_bar=True) |
| ZigZag API: estimates the cost of running the given workload on the given hardware architecture. More... | |
| tuple[float, float, float, float, list[tuple[CostModelEvaluationABC, Any]]] | get_hardware_performance_zigzag_imc (*Any args) |
| Overload with type hint. More... | |
1.9.1