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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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Class that dumps all received CMEs into a list and saves that list to a pickle file. More...


Public Member Functions | |
| def | __init__ (self, list[StageCallable] list_of_callables, *str pickle_filename, **Any kwargs) |
| def | run (self) |
| Run the simple save stage by running the substage and saving the CostModelEvaluation simple json representation. More... | |
Public Member Functions inherited from Stage | |
| def | __init__ (self, list["StageCallable"] list_of_callables, **Any kwargs) |
| def | __iter__ (self) |
| bool | is_leaf (self) |
Public Attributes | |
| pickle_filename | |
Public Attributes inherited from Stage | |
| kwargs | |
| list_of_callables | |
Class that dumps all received CMEs into a list and saves that list to a pickle file.
| def __init__ | ( | self, | |
| list[StageCallable] | list_of_callables, | ||
| *str | pickle_filename, | ||
| **Any | kwargs | ||
| ) |
| list_of_callables | see Stage |
| pickle_filename | output pickle filename |
| kwargs | any kwargs, passed on to substages |
| def run | ( | self | ) |
Run the simple save stage by running the substage and saving the CostModelEvaluation simple json representation.
This should be placed above a ReduceStage such as the SumStage, as we assume the list of CMEs is passed as extra_info
Reimplemented from Stage.

| pickle_filename |