1 code implementation • 8 Nov 2023 • Shikai Fang, Madison Cooley, Da Long, Shibo Li, Robert Kirby, Shandian Zhe
Machine learning based solvers have garnered much attention in physical simulation and scientific computing, with a prominent example, physics-informed neural networks (PINNs).
no code implementations • 31 Oct 2023 • Mingjie Liu, Teodor-Dumitru Ene, Robert Kirby, Chris Cheng, Nathaniel Pinckney, Rongjian Liang, Jonah Alben, Himyanshu Anand, Sanmitra Banerjee, Ismet Bayraktaroglu, Bonita Bhaskaran, Bryan Catanzaro, Arjun Chaudhuri, Sharon Clay, Bill Dally, Laura Dang, Parikshit Deshpande, Siddhanth Dhodhi, Sameer Halepete, Eric Hill, Jiashang Hu, Sumit Jain, Ankit Jindal, Brucek Khailany, George Kokai, Kishor Kunal, Xiaowei Li, Charley Lind, Hao liu, Stuart Oberman, Sujeet Omar, Ghasem Pasandi, Sreedhar Pratty, Jonathan Raiman, Ambar Sarkar, Zhengjiang Shao, Hanfei Sun, Pratik P Suthar, Varun Tej, Walker Turner, Kaizhe Xu, Haoxing Ren
ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design.
1 code implementation • 7 Feb 2023 • Hongsup Oh, Roman Amici, Geoffrey Bomarito, Shandian Zhe, Robert Kirby, Jacob Hochhalter
In this paper, we present a machine learning method for the discovery of analytic solutions to differential equations.
no code implementations • 14 May 2022 • Rajarshi Roy, Jonathan Raiman, Neel Kant, Ilyas Elkin, Robert Kirby, Michael Siu, Stuart Oberman, Saad Godil, Bryan Catanzaro
Deep Convolutional RL agents trained on this environment produce prefix adder circuits that Pareto-dominate existing baselines with up to 16. 0% and 30. 2% lower area for the same delay in the 32b and 64b settings respectively.
1 code implementation • 24 Feb 2022 • Da Long, Zheng Wang, Aditi Krishnapriyan, Robert Kirby, Shandian Zhe, Michael Mahoney
Physical modeling is critical for many modern science and engineering applications.
no code implementations • 16 Oct 2021 • Shibo Li, Zheng Wang, Akil Narayan, Robert Kirby, Shandian Zhe
the initialization, we only need to run the standard ODE solver twice -- one is forward in time that evolves a long trajectory of gradient flow for the sampled task; the other is backward and solves the adjoint ODE.
no code implementations • 6 Sep 2021 • Robert Kirby, Kolby Nottingham, Rajarshi Roy, Saad Godil, Bryan Catanzaro
In this work we augment state-of-the-art, force-based global placement solvers with a reinforcement learning agent trained to improve the final detail placed Half Perimeter Wire Length (HPWL).
no code implementations • 8 Jun 2020 • Zheng Wang, Wei Xing, Robert Kirby, Shandian Zhe
Deep kernel learning is a promising combination of deep neural networks and nonparametric function learning.
1 code implementation • 8 Jun 2020 • Zheng Wang, Wei Xing, Robert Kirby, Shandian Zhe
To address these issues, we propose Multi-Fidelity High-Order Gaussian Process (MFHoGP) that can capture complex correlations both between the outputs and between the fidelities to enhance solution estimation, and scale to large numbers of outputs.
2 code implementations • 2 Nov 2018 • Fitsum A. Reda, Guilin Liu, Kevin J. Shih, Robert Kirby, Jon Barker, David Tarjan, Andrew Tao, Bryan Catanzaro
We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows.
1 code implementation • ECCV 2018 • Fitsum A. Reda, Guilin Liu, Kevin J. Shih, Robert Kirby, Jon Barker, David Tarjan, Andrew Tao, Bryan Catanzaro
We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows.
Ranked #1 on Video Prediction on YouTube-8M
1 code implementation • 3 Aug 2018 • Raul Puri, Robert Kirby, Nikolai Yakovenko, Bryan Catanzaro
We provide a learning rate schedule that allows our model to converge with a 32k batch size.