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 |