Zobrazeno 1 - 10
of 9 800
pro vyhledávání: '"A. J. Darby"'
Autor:
Patel, Karan P., Maicke, Andrew, Arzate, Jared, Kwon, Jaesuk, Smith, J. Darby, Aimone, James B., Incorvia, Jean Anne C., Cardwell, Suma G., Schuman, Catherine D.
Novel devices and novel computing paradigms are key for energy efficient, performant future computing systems. However, designing devices for new applications is often time consuming and tedious. Here, we investigate the design and optimization of sp
Externí odkaz:
http://arxiv.org/abs/2411.01008
Traditional Monte Carlo methods for particle transport utilize source iteration to express the solution, the flux density, of the transport equation as a Neumann series. Our contribution is to show that the particle paths simulated within source iter
Externí odkaz:
http://arxiv.org/abs/2407.02295
Probabilistic artificial neural networks offer intriguing prospects for enabling the uncertainty of artificial intelligence methods to be described explicitly in their function; however, the development of techniques that quantify uncertainty by well
Externí odkaz:
http://arxiv.org/abs/2311.13038
In this paper we analyze the probability distributions associated with rolling (possibly unfair) dice infinitely often. Specifically, given a $q$-sided die, if $x_i\in\{0,\ldots,q-1\}$ denotes the outcome of the $i^{\text{th}}$ toss, then the distrib
Externí odkaz:
http://arxiv.org/abs/2309.07366
Autor:
Cardwell, Suma G., Schuman, Catherine D., Smith, J. Darby, Patel, Karan, Kwon, Jaesuk, Liu, Samuel, Allemang, Christopher, Misra, Shashank, Incorvia, Jean Anne, Aimone, James B.
Stochasticity is ubiquitous in the world around us. However, our predominant computing paradigm is deterministic. Random number generation (RNG) can be a computationally inefficient operation in this system especially for larger workloads. Our work l
Externí odkaz:
http://arxiv.org/abs/2212.00625
Autor:
Liu, Samuel, Kwon, Jaesuk, Bessler, Paul W., Cardwell, Suma, Schuman, Catherine, Smith, J. Darby, Aimone, James B., Misra, Shashank, Incorvia, Jean Anne C.
Probabilistic computing using random number generators (RNGs) can leverage the inherent stochasticity of nanodevices for system-level benefits. The magnetic tunnel junction (MTJ) has been studied as an RNG due to its thermally-driven magnetization dy
Externí odkaz:
http://arxiv.org/abs/2211.16588
Autor:
Theilman, Bradley H., Wang, Yipu, Parekh, Ojas D., Severa, William, Smith, J. Darby, Aimone, James B.
Finding the maximum cut of a graph (MAXCUT) is a classic optimization problem that has motivated parallel algorithm development. While approximate algorithms to MAXCUT offer attractive theoretical guarantees and demonstrate compelling empirical perfo
Externí odkaz:
http://arxiv.org/abs/2210.02588
Autor:
Rehm, Laura, Capriata, Corrado Carlo Maria, Shashank, Misra, Smith, J. Darby, Pinarbasi, Mustafa, Malm, B. Gunnar, Kent, Andrew D.
Publikováno v:
Phys. Rev. Applied 19, 024035 (2023)
True random number generators are of great interest in many computing applications such as cryptography, neuromorphic systems and Monte Carlo simulations. Here we investigate perpendicular magnetic tunnel junction nanopillars (pMTJs) activated by sho
Externí odkaz:
http://arxiv.org/abs/2209.01480
Autor:
Sandstead, Harold H. *, Wagner, Conrad **, 2
Publikováno v:
In The Journal of Nutrition June 2002 132(6):1103-1106
Autor:
Smith, J. Darby, Hill, Aaron J., Reeder, Leah E., Franke, Brian C., Lehoucq, Richard B., Parekh, Ojas, Severa, William, Aimone, James B.
Publikováno v:
Nature Electronics 2022
Computing stands to be radically improved by neuromorphic computing (NMC) approaches inspired by the brain's incredible efficiency and capabilities. Most NMC research, which aims to replicate the brain's computational structure and architecture in ma
Externí odkaz:
http://arxiv.org/abs/2107.13057