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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 keeps yields only the cost model evaluation that has minimal EDP of all cost model evaluations generated by it's substages created by list_of_callables. More...


Public Member Functions | |
| None | __init__ (self, list[StageCallable] list_of_callables, *bool reduce_minimal_keep_others=False, **Any kwargs) |
| Initialize the compare stage. More... | |
| def | run (self) |
| Run the compare stage by comparing a new cost model output with the current best found result. 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 | |
| keep_others | |
Public Attributes inherited from Stage | |
| kwargs | |
| list_of_callables | |
Class that keeps yields only the cost model evaluation that has minimal EDP of all cost model evaluations generated by it's substages created by list_of_callables.
| None __init__ | ( | self, | |
| list[StageCallable] | list_of_callables, | ||
| *bool | reduce_minimal_keep_others = False, |
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| **Any | kwargs | ||
| ) |
Initialize the compare stage.
| def run | ( | self | ) |
Run the compare stage by comparing a new cost model output with the current best found result.
Reimplemented from Stage.

| keep_others |