Abstract: The present disclosure relates to systems and methods for floorplanning using machine learning techniques. Embodiments may include receiving an electronic design and analyzing the electronic design using a reinforcement learning agent. Embodiments may further include recommending a first action wherein the first action includes at least one of a place agent action, a via agent action, or a route agent action. Embodiments may also include updating the electronic design based upon, at least in part, the first action to generate an updated electronic design. Embodiments may further include analyzing the updated electronic design using the reinforcement learning agent and recommending a second action wherein the second action includes at least one of a place agent action, a via agent action, or a route agent action. Embodiments may also include updating the updated electronic design based upon the second action to generate a second updated electronic design.

Abstract: The present disclosure relates to electronic circuit design, and more specifically, to determining the computational requirements of fully synthesizing a printed circuit board and/or package. Embodiments may include receiving, using a processor, one or more PCB electronic design files and determining whether the PCB electronic design files include data required for a synthesis engine. If any data is missing, the method may include inferring one or more parameters using an inference engine and providing the one or more parameters to the synthesis engine, wherein the synthesis engine includes at least one of a placement, via assignment, routing, and metal pouring processes. The method may also include collecting process data from the placement, via assignment, routing, and metal pouring processes and training a machine learning system using the process data.