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.
Classes | Namespaces | Variables
layer_node.py File Reference

Classes

class  LoopRelevancyInfo
 Per LayerOperand, store the Relevant, Irrelevant LayerDims, and which LayerDims are Partially Relevant to each other. More...
 
class  LayerNodeAttributes
 Packs the attributes needed to initialize a LayerNode. More...
 
class  MappingAttributes
 Packs the attributes needed to initialize a LayerNode, related to the mapping. More...
 
class  LayerNode
 Represents a single layer in a workload. More...
 

Namespaces

 zigzag.workload.layer_node
 

Variables

 logger = logging.getLogger(__name__)