Results 101 to 104 of about 104 (104)
This work harnesses nonidealities in analog in‐memory computing (IMC) by training physical neural networks modeled with ordinary differential equations. A differentiable spike‐time discretization accelerates training by 20× and reduces memory usage by 100×, enabling large IMC‐equivalent models to learn the CIFAR‐10 dataset.
Yusuke Sakemi+5 more
wiley +1 more source
Speech Recognition with Cochlea‐Inspired In‐Sensor Computing
Traditional speech recognition methods rely on software‐based feature extraction that introduces latency and high energy costs, making them unsuitable for low‐power devices. A proof‐of‐concept demonstration is provided of a bioinspired tonotopic sensor for speech recognition that mimics the human cochlea, using a spiral‐shaped elastic metamaterial. The
Paolo H. Beoletto+4 more
wiley +1 more source
ABSTRACT Selecting the optimal donor is crucial for optimizing results of allogeneic hematopoietic cell transplantation (allo‐HCT). We analyzed outcomes based on donor type in 2809 myelofibrosis (MF) patients undergoing first allo‐HCT between 2015 and 2021 at EBMT centers.
Juan Carlos Hernández‐Boluda+24 more
wiley +1 more source