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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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Represents the sum of multiple CMEs. More...


Public Member Functions | |
| def | __init__ (self) |
| def | __str__ (self) |
Public Member Functions inherited from CostModelEvaluationABC | |
| def | core (self) |
| "CumulativeCME" | __add__ (self, "CostModelEvaluationABC" other) |
| def | __mul__ (self, int number) |
| dict[str, float] | __simplejsonrepr__ (self) |
| Simple JSON representation used for saving this object to a simple json file. More... | |
| def | __jsonrepr__ (self) |
| JSON representation used for saving this object to a json file. More... | |
Public Attributes | |
| accelerator | |
Represents the sum of multiple CMEs.
This class only contains attributes that make sense for cumulated CMEs
| def __init__ | ( | self | ) |
Reimplemented from CostModelEvaluationABC.
| def __str__ | ( | self | ) |
| accelerator |