The invention relates to a unit statistical
time sequence prediction method based on a graph neural network, and the method comprises the steps: S1, obtaining a standard unit
netlist, extracting all transistors and other elements in the standard unit
netlist as a first type of nodes and a second type of nodes, building a first
edge based on the connection relation between the other elements and the transistors, and building a second
edge based on the connection relation between the first edge and the second edge; constructing an initial heterogeneous graph; s2, according to the basic principle of a circuit, simplifying the initial graph by using a redundant parasitic deletion method, restoring the first type of nodes deleted in the simplification process, and constructing virtual edges for the first type of nodes without direct connection elements to connect the nearest other first type of nodes to obtain a simplified heterogeneous graph; s3, according to element types corresponding to two nodes connected with each edge in the simplified heterogeneous graph, establishing an adjacent matrix for each type of connection, and constructing a corresponding
feature matrix; and S4, inputting the constructed
adjacency matrix and the
feature matrix into the trained heterogeneous graph
attention network to obtain a statistical
delay prediction result. Compared with the prior art, accurate and efficient statistical
time sequence representation can be carried out on the
standard cell library.