Distinct from traditional heuristic solvers, this paper on one hand proposes an RL-based model for mixed-size macro placement, which differs from existing learning-based placers that often consider the macro by coarse grid-based mask. While the standard cells are placed via gradient-based GPU acceleration. On the other hand, a one-shot conditional generative routing model, which is composed of a special-designed input-size-adapting generator and a bi-discriminator, is devised to perform one-shot routing to the pins within each net, and the order of nets to route is adaptively learned.

In this work, we identify dropout induced sparsity forLSTMs asasuitable mode ofcomputation reduction. Dropout isawidely usedregularization mechanism, which randomly drops computed neuron values during each iteration of training.

We present a novel compression algorithm for reducing the storage of LiDAR sensor data streams.

New deep learning-based approaches further improve the quality of synthetic textures.

Large language models (LLMs) have recently demonstrated great success in generating and understanding natural language. While they have also shown potential beyond the domain of natural language, it remains an open question as to what extent and in which way these LLMs can plan.