no code implementations • 28 Sep 2023 • Lakshmi Nair, David Widemann, Brad Turcott, Nick Moore, Alexandra Wleklinski, Darius Bunandar, Ioannis Papavasileiou, Shihu Wang, Eric Logan
Photonic computing promises faster and more energy-efficient deep neural network (DNN) inference than traditional digital hardware.
no code implementations • 12 May 2022 • Ayon Basumallik, Darius Bunandar, Nicholas Dronen, Nicholas Harris, Ludmila Levkova, Calvin Mccarter, Lakshmi Nair, David Walter, David Widemann
Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) inference than their digital counterparts.
1 code implementation • 22 Dec 2021 • Brian Bartoldson, Rui Wang, Yucheng Fu, David Widemann, Sam Nguyen, Jie Bao, Zhijie Xu, Brenda Ng
This raises the possibility of a fast, accurate replacement for a CFD simulator and therefore efficient approximation of the IAs required by CCS design optimization.
no code implementations • 13 Nov 2020 • Youngkyu Kim, Youngsoo Choi, David Widemann, Tarek Zohdi
Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i. e., the solution space has a small Kolmogorov n-width.
no code implementations • 25 Sep 2020 • Youngkyu Kim, Youngsoo Choi, David Widemann, Tarek Zohdi
A speedup of up to 2. 6 for 1D Burgers' and a speedup of 11. 7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique.