Zobrazeno 1 - 10
of 678
pro vyhledávání: '"P. Aimone"'
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
Co-design is a prominent topic presently in computing, speaking to the mutual benefit of coordinating design choices of several layers in the technology stack. For example, this may be designing algorithms which can most efficiently take advantage of
Externí odkaz:
http://arxiv.org/abs/2312.14954
Autor:
Wolpert, David, Korbel, Jan, Lynn, Christopher, Tasnim, Farita, Grochow, Joshua, Kardeş, Gülce, Aimone, James, Balasubramanian, Vijay, de Giuli, Eric, Doty, David, Freitas, Nahuel, Marsili, Matteo, Ouldridge, Thomas E., Richa, Andrea, Riechers, Paul, Roldán, Édgar, Rubenstein, Brenda, Toroczkai, Zoltan, Paradiso, Joseph
Publikováno v:
PNAS 121 (45) e2321112121 (2024)
The relationship between the thermodynamic and computational characteristics of dynamical physical systems has been a major theoretical interest since at least the 19th century, and has been of increasing practical importance as the energetic cost of
Externí odkaz:
http://arxiv.org/abs/2311.17166
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
Autor:
Kudithipudi, Dhireesha, Daram, Anurag, Zyarah, Abdullah M., Zohora, Fatima Tuz, Aimone, James B., Yanguas-Gil, Angel, Soures, Nicholas, Neftci, Emre, Mattina, Matthew, Lomonaco, Vincenzo, Thiem, Clare D., Epstein, Benjamin
Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The development of lifelong learning algorithms could lead to a range of
Externí odkaz:
http://arxiv.org/abs/2310.04467
A satisfactory understanding of information processing in spiking neural networks requires appropriate computational abstractions of neural activity. Traditionally, the neural population state vector has been the most common abstraction applied to sp
Externí odkaz:
http://arxiv.org/abs/2306.16684
Biocompatibility and bone regeneration with elastin-like recombinamer-based catalyst-free click gels
Autor:
I. N. Camal Ruggieri, M. Aimone, D. Juanes-Gusano, A. Ibáñez-Fonseca, O. Santiago, M. Stur, J. P. Mardegan Issa, L. R. Missana, M. Alonso, J. C. Rodríguez-Cabello, S. Feldman
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Large bone defects are a significant health problem today with various origins, including extensive trauma, tumours, or congenital musculoskeletal disorders. Tissue engineering, and in particular bone tissue engineering, aims to respond to t
Externí odkaz:
https://doaj.org/article/43c0d8475905487fa0a961e107d0df75
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:
Talenti, Francesco Rinaldo, Wabnitz, Stefan, Ghorbel, Inès, Combrié, Sylvain, Aimone-Giggio, Luca, De Rossi, Alfredo
Publikováno v:
Physical Review A 106 023505 (2022)
We introduce a numerical procedure which permits to drastically accelerate the design of multimode photonic crystal resonators. Specifically, we demonstrate that the optical response of an important class of such nanoscale structures is reproduced ac
Externí odkaz:
http://arxiv.org/abs/2210.12017