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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 is the complete list of members for LayerDimSizes, including all inherited members.
| __add__(self, "LayerDimSizes" other) | LayerDimSizes | |
| __contains__(self, Any key) | LayerAttribute | |
| __delitem__(self, LayerDim key) | LayerDimSizes | |
| __eq__(self, object other) | LayerAttribute | |
| __getitem__(self, Any key) | LayerAttribute | |
| __hash__(self) | LayerAttribute | |
| __init__(self, dict[LayerDim, UnrollFactor] data) | LayerDimSizes | |
| zigzag::workload::layer_attribute::LayerAttribute.__init__(self, Any data) | LayerAttribute | |
| __iter__(self) | LayerAttribute | |
| __jsonrepr__(self) | LayerAttribute | |
| __len__(self) | LayerAttribute | |
| __repr__(self) | LayerAttribute | |
| __setitem__(self, LayerDim key, UnrollFactor value) | LayerDimSizes | |
| __str__(self) | LayerAttribute | |
| copy(self) | LayerDimSizes | |
| data | LayerDimSizes | |
| items(self) | LayerDimSizes | |
| layer_dims(self) | LayerDimSizes | |
| total_size(self) | LayerDimSizes |