Abstract: According to one embodiment, a method, computer system, and computer program product for automatic parameter control of integrated circuit design flows is provided. The present invention may include training a machine learning model through Reinforcement Learning using training data to generate a trained machine learning model, wherein the training data comprises previously executed design flow stages for different specific circuit designs, wherein the previously executed design flow stages comprise a sequence of flow stages, original input states, input parameters for each flow stage, and a quality of result (QoR) for an output state of a final flow stage within the sequence of flow stages; and processing unexecuted design flow stages for the different specific circuit designs through the trained machine learning model to generate an optimal set of input parameters for each flow stage in the unexecuted design flow stages for the different specific circuit designs.

Abstract: Disclosed herein is a computer implemented method of correcting a timing failure of a network of conductors and repowering structures in an integrated circuit design using a reinforcement learning agent. The reinforcement learning agent comprises a neural network. The method comprises: receiving a graph comprising nodes and edges that encodes said network of conductors and repowering structures; and receiving a modification recommendation from said reinforcement learning agent in response to inputting said graph into said reinforcement learning agent.

Abstract: Disclosed herein is a computer implemented method of correcting a timing failure of a network of conductors and repowering structures in an integrated circuit design using a reinforcement learning agent. The reinforcement learning agent comprises a neural network. The method comprises: receiving a graph comprising nodes and edges that encodes said network of conductors and repowering structures; and receiving a modification recommendation from said reinforcement learning agent in response to inputting said graph into said reinforcement learning agent.

Abstract: Disclosed herein is a computer implemented method of correcting a timing failure of a network of conductors and repowering structures in an integrated circuit design using a reinforcement learning agent. The reinforcement learning agent comprises a neural network. The method comprises: receiving a graph comprising nodes and edges that encodes said network of conductors and repowering structures; and receiving a modification recommendation from said reinforcement learning agent in response to inputting said graph into said reinforcement learning agent.