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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 MemoryLevel, including all inherited members.
| __eq__(self, Any other) | MemoryLevel | |
| __hash__(self) | MemoryLevel | |
| __init__(self, MemoryInstance memory_instance, list[MemoryOperand] operands, dict[MemoryOperand, int] mem_level_of_operands, PortAllocation port_alloc, ServedMemDimensions served_dimensions, OperationalArrayABC operational_array, int identifier) | MemoryLevel | |
| __jsonrepr__(self) | MemoryLevel | |
| __repr__(self) | MemoryLevel | |
| __str__(self) | MemoryLevel | |
| bandwidths_max | MemoryLevel | |
| bandwidths_min | MemoryLevel | |
| formatted_string | MemoryLevel | |
| get_max_bandwidth(self, MemoryOperand operand, DataDirection data_dir) | MemoryLevel | |
| get_min_bandwidth(self, MemoryOperand operand, DataDirection data_dir) | MemoryLevel | |
| has_same_performance(self, "MemoryLevel" other) | MemoryLevel | |
| id | MemoryLevel | |
| mem_level_of_operands | MemoryLevel | |
| memory_instance | MemoryLevel | |
| name | MemoryLevel | |
| oa_dim_sizes | MemoryLevel | |
| operands | MemoryLevel | |
| port_alloc_raw | MemoryLevel | |
| ports | MemoryLevel | |
| read_energy | MemoryLevel | |
| served_dimensions | MemoryLevel | |
| unroll_count(self) | MemoryLevel | |
| write_energy | MemoryLevel |