Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scalability limits in terms of performance and power are being reached, alternative computing paradigms with collocated computation and storage are actively being sought. A fascinating such approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. We present an experimental demonstration using one million phase change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Its result is imprinted in the conductance states of the memory devices. The results of using such a computational memory for processing real-world data sets show that this co-existence of computation and storage at the nanometer scale could enable ultra-dense, low-power, and massively-parallel computing systems.
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http://dx.doi.org/10.1038/s41467-017-01481-9 | DOI Listing |
J Mol Model
January 2025
Escuela Superior de Física y Matemáticas, IPN S/N, Edificio 9 de la Unidad Profesional "Adolfo López Mateos", Col. Lindavista, Alc. Gustavo A. Madero, 07738, Mexico City, Mexico.
Context: "Nanostructure of graphene-reinforced with polymethyl methacrylate" (PMMA-G), and vice versa, is investigated using its molecular structure, in the present work. The PMMA-G nanostructure was constructed by bonding PMMA with graphene nanosheet in a sense to get three different configurations. Each configuration consisted of polymeric structures with three degrees of polymerization (such as monomers, dimers, and trimers polymers, respectively).
View Article and Find Full Text PDFBMC Neurol
January 2025
Graduate School of Physical Education, Myongji University, Mingzhi Road, Churen District, Yongin, 17058, Gyeonggi Province, Republic of Korea.
Background: This study evaluates the comprehensive impact of different exercise interventions on the quality of life in stroke patients through network meta-analysis, aiming to provide scientific evidence for developing more effective rehabilitation programs and improving patients' physical, psychological, and social functions.
Methods: This systematic review, registered in PROSPERO (CRD42024541517) and following PRISMA guidelines, searched multiple databases (PubMed, Web of Science, EMbase, Cochrane, Ebsco) until November 1, 2024. Studies were selected based on the PICOS criteria, including RCTs on stroke and exercise.
Sci Adv
January 2025
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
Neurological disorders are a substantial global health burden, affecting millions of people worldwide. A key challenge in developing effective treatments and preventive measures is the realization of low-power wearable systems with early detection capabilities. Traditional strategies rely on machine learning algorithms, but their computational demands often exceed what miniaturized systems can provide.
View Article and Find Full Text PDFPLoS One
January 2025
Computer Engineering, CCSIT, King Faisal University, Al Hufuf, Kingdom of Saudi Arabia.
The health of poultry flock is crucial in sustainable farming. Recent advances in machine learning and speech analysis have opened up opportunities for real-time monitoring of the behavior and health of flock. However, there has been little research on using Tiny Machine Learning (Tiny ML) for continuous vocalization monitoring in poultry.
View Article and Find Full Text PDFNanotechnology
January 2025
Department of Physics, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang Area, Shanghai 200240, Shanghai, 200240, CHINA.
Both stability and multi-level switching are crucial performance aspects for resistive random-access memory (RRAM), each playing a significant role in improving overall device performance. In this study, we successfully integrate these two features into a single RRAM configuration by embedding Ag-nanoparticles (Ag-NPs) into the TiN/Ta2O5/ITO structure. The device exhibits substantially lower switching voltages, a larger switching ratio, and multi-level switching phenomena compared to many other nanoparticle-embedded devices.
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