Results 91 to 100 of about 1,050,120 (328)

Possibilities of using of hardware accelerators for intrusion detection and prevention systems

Авіаційно-космічна техніка та технологія
The subject of this study is the capabilities of FPGA technology for cybersecurity solutions with the network interface accelerators of SmartNIC, as well as the technologies for building, deploying, supporting, and accelerating intrusion detection ...
Artem Tetskyi, Artem Perepelitsyn
doaj   +1 more source

Surface Tension Measurement of Ti‐6Al‐4V by Falling Droplet Method in Oxygen‐Free Atmosphere

Advanced Engineering Materials, EarlyView.
In this article, the temperature‐dependent surface tension of free falling, oscillating Ti‐6Al‐4V droplets is investigated in both argon and monosilane doped, oxygen‐free atmosphere. Droplet temperature and oscillation are captured with one single high‐speed camera, and the surface tension is calculated with Rayleigh's formula.
Johannes May, Finn‐Lennard Janthur, Lena Kreie, Michael Müller, Sarah Seffer, Nick Schwarz, Jörg Hermsdorf, Thomas Hassel, Stefan Kaierle, Ludger Overmeyer   +9 more
wiley   +1 more source

SOM Hardware-Accelerator

, 1997
Many applications of Selforganizing Feature Maps (SOMs) need a high performance hardware system in order to be efficient. Because of the regular and modular structure of SOMs , a hardware realization is obvious. Based on the idea of a massively parallel system, several chips have been designed, manufactured and tested by the authors.
Rüping, Stefan, Porrmann, Mario, Rückert, Ulrich   +2 more
openaire   +2 more sources

FPGA-Based CNN Inference Accelerator Synthesized from Multi-Threaded C Software

, 2018
A deep-learning inference accelerator is synthesized from a C-language software program parallelized with Pthreads. The software implementation uses the well-known producer/consumer model with parallel threads interconnected by FIFO queues.
Anderson, Jason H., Brothers, John, Grady, Brett, Kim, Jin Hee, Lian, Ruolong   +4 more
core   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

The CBE Hardware Accelerator for Numerical Relativity: A Simple Approach

, 2009
Hardware accelerators (such as the Cell Broadband Engine) have recently received a significant amount of attention from the computational science community because they can provide significant gains in the overall performance of many numerical ...
Khanna, Gaurav
core   +1 more source

Supraparticles Composed of Graphitic Carbon Nitride Nanoparticles and Silica‐Supported Horseradish Peroxidase as Customizable Hybrid Catalysts for Photo‐Biocatalytic Cascade Reactions in Continuous Flow

Advanced Functional Materials, EarlyView.
Herein presented supraparticles combine the nanoparticulate photocatalyst graphitic carbon nitride with the enzyme horseradish peroxidase, which is immobilized on silica nanoparticles. In an optimized compatibility range, both catalysts operate effectively within the hybrid supraparticles and catalyze a cascade reaction consisting of the photocatalytic
Bettina Herbig, Egzon Cermjani, Doris Hanselmann, Johannes Prieschl, Angelika Schmitt, Michael S. Moritz, Christian Papp, Greta Nölke, Stefano Di Fiore, Christoph Deckers, Thomas H. Rehm, Susanne Wintzheimer   +11 more
wiley   +1 more source

Design of a Low-area Digit Recognition Accelerator Using MNIST Database

JOIV: International Journal on Informatics Visualization, 2022
Deep neural networks, which is a field of artificial intelligence, have been used in various fields. Deep learning is processed on high-performance GPUs or TPUs. It requires high cost as much as its good performance.
Joonyub Kwon, Sunhee Kim
doaj   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Survey of FPGA based recurrent neural network accelerator

网络与信息安全学报, 2019
Recurrent neural network(RNN) has been used wildly used in machine learning field in recent years,especially in dealing with sequential learning tasks compared with other neural network like CNN.However,RNN and its variants,such as LSTM,GRU and other ...
Chen GAO, Fan ZHANG
doaj   +3 more sources