Results 51 to 60 of about 3,717 (263)

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

Hardware accelerators for processing clusters in binary vectors [PDF]

ITM Web of Conferences
The paper suggests fast hardware accelerators for discovering clusters of zeros and/or ones in binary vectors. Any cluster is composed of successive bits with the same value (either 1 or 0). Search for such segments is required in many practical problems,
Skliarova Iouliia, Skliarov Valeri
doaj   +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

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

Application-Specific Instruction-Set Processors (ASIPs) for Deep Neural Networks: A Survey

IEEE Access
The demand for artificial intelligence applications is rising in every field. And with this demand, machine learning algorithms and techniques are becoming more complicated and compute-intensive.
Muhammad Ali, Diana Gohringer
doaj   +1 more source

From Wafers to Electrodes: Transferring Automatic Optical Inspection (AOI) for Multiscale Characterization of Smart Battery Manufacturing

Advanced Functional Materials, EarlyView.
Automat optical inspection (AOI) techniques in semiconductor fabrication can be leveraged in battery manufacturing, enabling scalable detection and analysis of electrode‐ and cell‐level imperfections through AI‐driven analytics and a digital‐twin framework.
Jianyu Li, Ertao Hu, Wei Wei, Feifei Shi
wiley   +1 more source

A High-Level Synthesis Library for Synthesizing Efficient and Functional-Safe CNN Dataflow Accelerators

IEEE Access
Convolution neural networks (CNNs) are widely applied in many machine learning applications. Hardware acceleration for CNNs is crucial, given their high computational intensity and the demand for enhanced energy efficiency and reduced latency in ...
Dionysios Filippas, Christodoulos Peltekis, Vasileios Titopoulos, Ioannis Kansizoglou, Georgios CH. Sirakoulis, Antonios Gasteratos, Giorgos Dimitrakopoulos   +6 more
doaj   +1 more source

High‐Resolution and Real‐Time In Situ Generation of Cellular Spheroids by Laser‐Assisted Bioprinting for Guided Microvascularization

Advanced Functional Materials, EarlyView.
Micro‐injection laser‐assisted bioprinting enables ultrafast and precise patterning of small endothelial cell spheroids by injecting a highly concentrated single‐cell suspension into GelMA/ColMA hydrogels. In co‐culture with fibroblasts, controlled pre‐vasculogenic network formation is obtained at microscale resolution.
Charles Handschin, Katharina Wulz, Clotilde Jugie, Marie‐Laure Stachowicz, Shane Fennell, Aurélien Mazet, Malou Lea, Nathalie Dusserre, Jean‐Christophe Fricain, Hugo Oliveira   +9 more
wiley   +1 more source

Hardware Accelerated Graph Analytics

, 2022
Hardware Accelerated Graph ...
Deepak, Kumar, Shaler, Michael
openaire   +1 more source

Temperature‐Modulated Threshold Response in a Volatile Memristor: Toward a Biomimetic Polymodal Nociceptive System

Advanced Functional Materials, EarlyView.
This study demonstrates an artificial polymodal nociceptor whose firing threshold is actively modulated by temperature. A volatile TiN/TiOx/ZnO/TiOx/ITO memristor shows interfacial ion–driven resistive switching and membrane‐potential‐like dynamics, enabling temperature‐dependent nociceptive behavior.
Chanmin Hwang, Yuseong Jang, Myoungsu Chae, Hee‐Dong Kim   +3 more
wiley   +1 more source