Embracing Machine Learning in EDA

Abstract

The application of machine learning (ML) in EDA is a hot research trend. To use ML in EDA, it is nature to think from the ML method point of view, i.e. supervised learning, reinforcement learning and unsupervised learning. Based on this point of view, we can roughly classify the ML applications in EDA into three categories: prediction, optimization, and generation. The prediction category applies supervised learning methods to predict design quality of result (QoR) metrics. There are two kinds of QoR metrics that benefit from the prediction. One kind of metrics are those that can be determined at the current design stage but calculating them consumes a lot of computing resources. For ex-ample, [11] [12] leverage ML to predict circuit power consumption without expensive simulations. The other kind of metrics are those that depend on future design stages. For example, [8] predicts post layout parasitics from schematic of analog circuits. The optimization category applies Bayesian Optimization (BO)and reinforcement learning (RL) to directly optimize EDA problems.BO treats the optimization objective as a blackbox function and tries to find optimal solutions by iteratively sampling the solution space. For example, [5] proposes to use BO with graph embedding and neural network-based surrogate model to size analog circuits. RL treats the optimization objective as the reward from an environment, and trains agents to maximize the reward. [7] proposes to use RL to optimize macro placement, and [9] proposes to use RL to optimize parallel prefix circuit structures. The generation category applies generative models such as generative adversarial networks (GANs) to directly generate solutions to EDA problems. Generative models can learn from previous optimized data distribution and generate solutions for a new problem instance without going through iterative processes like BO or RL. For example, [10] builds a conditional GAN model that learns to generate optical proximity correction (OPC) layout from the original mask.