Slang Shading Language Advances

In this talk, Yong He, a Senior Researcher at NVIDIA, shares recent advances and new features in the Slang shading language.

Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks

Lithography simulation is a critical step in VLSI design and optimization for manufacturability. Existing solutions for highly accurate lithography simulation with rigorous models are computationally expensive and slow, even when equipped with various approximation techniques. Recently, machine learning has provided alternative solutions for lithography simulation tasks such as coarse-grained edge placement error regression and complete contour prediction. However, the impact of these learning-based methods has been limited due to restrictive usage scenarios or low simulation accuracy.

GATSPI: GPU Accelerated Gate-Level Simulation for Power Improvement

(DAC 2022 preprint)

In this paper, we present GATSPI, a novel GPU accelerated logic gate simulator that enables ultra-fast power estimation for industry sized ASIC designs with millions of gates. GATSPI is written in PyTorch with custom CUDA kernels for ease of coding and maintainability. It achieves simulation kernel speedup of up to 1668X on a single-GPU system and up to 7412X on a multiple-GPU system when compared to a commercial gate-level simulator running on a single CPU core.

Harini Muthukrishnan

Harini joined NVIDIA in February 2022 as a part of the System Architecture Research Group. Her research focuses on developing GPU and interconnect solutions for scalable multi-GPU systems. 

Yufan He

Yufan He joined NVIDIA as an applied research scientist in 2021. His current research interests are AutoML, computer vision, and medical image analysis.

Before that, Yufan received his M.S. and Ph.D. in ECE from the Johns Hopkins University in 2021 and B.S. in EE from Tsinghua University in 2016.

Seungjun Nah

Seungjun Nah is a research scientist at NVIDIA.

He received his Ph.D. in Electrical and Computer Engineering at Seoul National University, advised by Kyoung Mu Lee.

His research interests include computer vision with focus on high-quality image generation and the fast execution of such algorithms. 

For more information, please visit: https://seungjunnah.github.io/