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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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Functions | |
| def | get_spatial_loops (CostModelEvaluation cme) |
| def | get_temporal_loops (CostModelEvaluation cme) |
| def | get_memory_names (CostModelEvaluation cme) |
Variables | |
| TypeAlias | |
| def zigzag.mapping.utils.get_memory_names | ( | CostModelEvaluation | cme | ) |


| def zigzag.mapping.utils.get_spatial_loops | ( | CostModelEvaluation | cme | ) |

| def zigzag.mapping.utils.get_temporal_loops | ( | CostModelEvaluation | cme | ) |


| TypeAlias |