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.
ConvParser Member List

This is the complete list of members for ConvParser, including all inherited members.

__init__(self, int node_id, NodeProto node, dict[int, Any] nodes_outputs, list[dict[str, Any]] mapping_data, ModelProto onnx_model)ConvParser
zigzag::parser::onnx::onnx_operator_parser::ONNXOperatorParser.__init__(self, int node_id, NodeProto node, dict[int, Any] nodes_outputs, ModelProto onnx_model, *list[dict[str, Any]]|None mapping_data=None, Accelerator|None accelerator=None)ONNXOperatorParser
acceleratorONNXOperatorParser
CUSTOM_ACT_SIZE_ATTRONNXOperatorParserstatic
CUSTOM_OUTPUT_SIZE_ATTRONNXOperatorParserstatic
CUSTOM_WEIGHT_SIZE_ATTRONNXOperatorParserstatic
generate_layer_node_for_conv(self)ConvParser
get_activation_precision(self)ONNXOperatorParser
get_input_output_weight_data_type(self)ONNXOperatorParser
get_intermediate_output_precision(self)ONNXOperatorParser
get_layer_node_user_format(self, list[int] kernel_shape, list[int] strides, list[int] dilations, int group_size, list[int] padding, list[int] ia_shape, list[int] oa_shape)ConvParser
get_node_predecessors(self)ONNXOperatorParser
get_operand_source_user_format(self, list[int] predecessors)ONNXOperatorParser
get_weight_name(self, NodeProto node)ONNXOperatorParser
get_weight_precision(self)ONNXOperatorParser
mapping_dataConvParser
nodeONNXOperatorParser
node_idONNXOperatorParser
nodes_outputsONNXOperatorParser
onnx_modelConvParser
run(self)ConvParser