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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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This class captures multi-dimensional operational array size. More...


Public Member Functions | |
| def | __init__ (self, OperationalUnit operational_unit, dict[OADimension, int] dimension_sizes) |
| def | __jsonrepr__ (self) |
| bool | __eq__ (self, Any other) |
Public Member Functions inherited from OperationalArrayABC | |
| def | __init__ (self, dict[OADimension, int] dimension_sizes) |
Public Attributes | |
| total_unit_count | |
| total_area | |
| unit | |
| dimension_sizes | |
Public Attributes inherited from OperationalArrayABC | |
| dimension_sizes | |
This class captures multi-dimensional operational array size.
| def __init__ | ( | self, | |
| OperationalUnit | operational_unit, | ||
| dict[OADimension, int] | dimension_sizes | ||
| ) |
| operational_unit | an OperationalUnit object including precision and single operation energy, later we can add idle energy also (e.g. for situations that one or two of the input operands is zero). |
| dimensions | define the name and size of each multiplier array dimensions, e.g. {'D1': 3, 'D2': 5}. |
| bool __eq__ | ( | self, | |
| Any | other | ||
| ) |
| def __jsonrepr__ | ( | self | ) |

| dimension_sizes |
| total_area |
| total_unit_count |
| unit |