Daqi Lin

Heng Yang

Heng Yang is a Research Scientist in the Autonomous Vehicle Research group at NVIDIA. He is broadly interested in the algorithmic foundations of robot perception, action, and learning. His research vision is to enable safe and trustworthy autonomy for a broad range of high-integrity robotics applications, by designing tractable and provably correct algorithms that enjoy rigorous performance guarantees, developing fast implementations, and validating them on real robotic systems.

Bowen Wen

I am a Senior Research Scientist at NVIDIA Research. My research areas include robotic perception, computer vision. Specifically, I work on 3D visual perception and learning to facilitate robotic manipulations. I obtained my PhD degree in Computer Science from Rutgers University in 2022, advised by Prof. Kostas Bekris.

Frank Wang

Research Director, Deep Learning and Computer Vision, NVIDIA

Professor, Department of Electrical Engineering, National Taiwan University

Nikola Kovachki

Xinshuo Weng

Xinshuo Weng is a research scientist in the Autonomous Vehicle Research Group working with Marco Pavone. Prior to joining NVIDIA Research, she received a Ph.D.

Kaichun Mo

I am currently a Research Scientist at Seattle Robotics Lab under Prof. Dieter Fox, NVIDIA Research. I obtained my Ph.D. in Computer Science from Stanford University advised by Prof. Leonidas J. Guibas in 2022. I was affiliated with the Geometric Computation Group and Artificial Intelligence Lab at Stanford.

Real-Time Path Tracing and Beyond

In this keynote presented at High Performance Graphics 2022, Petrik Clarberg shares an update on real-time path tracing and the next steps for real-time graphics research.

Abstract

Routability-Aware Placement for Advanced FinFET Analog Circuits with Satisfiability Modulo Theories

Due to the increasingly complex design rules and geometric layout constraints within advanced FinFET nodes, automated placement of full-custom analog/mixed-signal (AMS) designs has become increasingly challenging. Compared with traditional planar nodes, AMS circuit layout is dramatically different for FinFET technologies due to strict design rules and grid-based restrictions for both placement and routing. This limits previous analog placement approaches in effectively handling all of the new constraints while adhering to the new layout style.

AutoCRAFT: Layout Automation for Custom Circuits in Advanced FinFET Technologies

Despite continuous efforts in layout automation for full-custom circuits, including analog/mixed-signal (AMS) designs, automated layout tools have not yet been widely adopted in current industrial full-custom design flows due to the high circuit complexity and sensitivity to layout parasitics. Nevertheless, the strict design rules and grid-based restrictions in nanometer-scale FinFET nodes limit the degree of freedom in full-custom layout design and thus reduce the gap between automation tools and human experts.

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