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Toward Intelligent Physical Design: Deep Learning and GPU Acceleration

Deep learning (DL) has achieved tremendous success in computer vision, natural language processing and gaming. Would DL help push physical design toward a more intelligent paradigm to meet the post-Moore era design automation challenges? We will discuss several supervised deep learning models and our works applying these models in physical design and related EDA domains. In addition to supervised learning, we will also illustrate that reinforcement learning and unsupervised learning with deep learning can be applied to design automation tasks. Furthermore, we will show the potential of GPU accelerated physical design with an example of placement engines implemented with deep learning frameworks such as TensorFlow and PyTorch. With deep learning and GPU accelerated algorithms, we could make physical design more intelligent.