Buffering is a prevalent interconnect optimization technique to help timing closure and is often performed after placement. A common buffering approach is to construct a Steiner tree and then buffers are inserted on the tree based on Ginneken-Lillis style algorithm. Such an approach is difficult to scale with large nets. Our work attempts to solve this problem with a generative machine-learning (ML) approach without Steiner tree construction. Our approach can extract and reuse knowledge from high quality samples and therefore has significantly improved scalability.

Macro placement is a critical very large-scale integration (VLSI) physical design problem that significantly impacts the design powerperformance-area (PPA) metrics. This paper proposes AutoDMP, a methodology that leverages DREAMPlace, a GPU-accelerated placer, to place macros and standard cells concurrently in conjunction with automated parameter tuning using a multi-objective hyperparameter optimization technique.

Standard cells are essential components of modern digital circuit designs. With process technologies advancing beyond the 5nm node, more routability issues have arisen due to the decreasing number of routing tracks, increasing number and complexity of design rules, and strict patterning rules. Automatic standard cell synthesis tools are struggling to design cells with severe routability issues.

DREAMPlace is a renowned open-source placer that provides GPUacceleratable infrastructure for placements of Very-Large-ScaleIntegration (VLSI) circuits. However, due to its limited focus on wirelength and density, existing placement solutions of DREAMPlace are not applicable to industrial design flows. To improve DREAMPlace towards commercial-quality without knowing the black-boxed algorithms of the tools, in this paper, we present DREAM-GAN, a placement optimization framework that advances DREAMPlace using generative adversarial learning.

Detailed routing is the most tedious and complex procedure in design automation and has become a determining factor in layout automation in advanced manufacturing nodes. Despite continuing advances in custom integrated circuit (IC) routing research, industrial custom layout flows remain heavily manual due to the high complexity of the custom IC design problem.

Evaluating the safety of an autonomous vehicle (AV) depends on the behavior of surrounding agents which can be heavily influenced by factors such as environmental context and informally-defined driving etiquette. A key challenge is in determining a minimum set of assumptions on what constitutes reasonable foreseeable behaviors of other road users for the development of AV safety models and techniques.

Simulation is the key to scaling up validation and verification for robotic systems such as autonomous vehicles. Despite advances in high-fidelity physics and sensor simulation, a critical gap remains in simulating realistic behaviors of road users. This is because, unlike simulating physics and graphics, devising first principle models for human-like behaviors is generally infeasible. In this work, we take a data-driven approach and propose a method that can learn to generate traffic behaviors from real-world driving logs.

Learning-based behavior prediction methods are increasingly being deployed in real-world autonomous systems, e.g., in fleets of self-driving vehicles, which are beginning to commercially operate in major cities across the world. Despite their advancements, however, the vast majority of prediction systems are specialized to a set of well-explored geographic regions or operational design domains, complicating deployment to additional cities, countries, or continents. Towards this end, we present a novel method for efficiently adapting behavior prediction models to new environments.

Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles. Typical heuristic-based traffic models offer flexible control to make vehicles follow specific trajectories and traffic rules. On the other hand, data-driven approaches generate realistic and human-like behaviors, improving transfer from simulated to real-world traffic. However, to the best of our knowledge, no traffic model offers both controllability and realism.

Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles. Recent deep learning models have shown impressive results for predicting occluded agents based on the behaviour of nearby visible agents; however, as we show in experiments, these models are difficult to integrate into downstream planning. To this end, we propose Bi-level Variational Occlusion Models (BiVO), a two-step generative model that first predicts likely locations of occluded agents, and then generates likely trajectories for the occluded agents.