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1.9.1
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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 a collection of Operational Array Dimensions (served by some Memory Instance) More...
Public Member Functions | |
| def | __init__ (self, set[OADimension] data) |
| def | nb_dims (self) |
| def | __eq__ (self, Any other) |
| def | __str__ (self) |
| def | __contains__ (self, OADimension other) |
| def | __iter__ (self) |
| def | __len__ (self) |
Public Attributes | |
| data | |
Represents a collection of Operational Array Dimensions (served by some Memory Instance)
| def __init__ | ( | self, | |
| set[OADimension] | data | ||
| ) |
| def __contains__ | ( | self, | |
| OADimension | other | ||
| ) |
| def __eq__ | ( | self, | |
| Any | other | ||
| ) |
| def __iter__ | ( | self | ) |
| def __len__ | ( | self | ) |
| def __str__ | ( | self | ) |
| def nb_dims | ( | self | ) |
| data |
1.9.1