Integrating machine learning delay estimation in fpga-based emulation systems

Abstract

A method or system for estimating delays in design under tests (DUTs) using machine learning. The system accesses multiple DUTs, each comprising various logic blocks. For each DUT, a combinatorial path is identified, connecting one or more logic blocks. A feature vector is generated, including values of orthogonal features representing the combinatorial path's characteristics. Each DUT is compiled for emulation, and the delay of its combinatorial path is measured. These measured delays, along with the corresponding feature vectors, are used to train a machine learning delay model. The trained model is designed to receive a combinatorial path of a DUT as input and generate an estimated wire delay as output. This approach leverages machine learning to predict delays in electronic designs, improving the efficiency and accuracy of delay estimations in complex circuits.