Results 61 to 70 of about 931,340 (223)

In-memory computing on a photonic platform

Science Advances, 2019
Nonvolatile multilevel phase-change memories on integrated photonic devices enable all-optical direct in-memory multiplications.
Ríos, Carlos, Youngblood, Nathan, Cheng, Zengguang, Le Gallo, Manuel, Pernice, Wolfram H. P., Wright, C. David, Sebastian, Abu, Bhaskaran, Harish   +7 more
openaire   +5 more sources

Advances in Infrared Detectors for In-Memory Sensing and Computing

Photonics
In-memory sensing and computing devices integrate the functionalities of sensors, memory, and processors, offering advantages such as low power consumption, high bandwidth, and zero latency, making them particularly suitable for simulating synaptic ...
Weibo Feng, Tianling Qin, Xin Tang
doaj   +1 more source

In Situ Quantization with Memory‐Transistor Transfer Unit Based on Electrochemical Random‐Access Memory for Edge Applications

Advanced Science
In‐memory computing based on nonvolatile synaptic arrays with computing functions has significantly improved the computing energy efficiency of neural networks.
Zhen Yang, Yuxiang Yang, Baiqian Wang, Yaoyu Tao, Zelun Pan, Lei Cai, Teng Zhang, Longhao Yan, Xianbin Li, Yuchao Yang   +9 more
doaj   +1 more source

Reliable Multistate RRAM Devices for Reconfigurable CAM and IMC Applications

IEEE Journal of the Electron Devices Society
This work presents a reliable multistate RRAM device based on a Cu/Ta2O5/WO ${}_{\text {3-x}}$ /Pt structure, utilizing fully CMOS-compatible materials. The device demonstrates four distinct resistive states under varying switching voltages, achieving a ...
Shengpeng Xing, Zijian Wang, Zhen Wang, Pengtao Li, Xuemeng Fan, Ziyang Zhang, Guobin Zhang, Jianhao Kan, Qi Luo, Shuai Zhong, Yishu Zhang   +10 more
doaj   +1 more source

Accurate deep neural network inference using computational phase-change memory

Nature Communications, 2020
Designing deep learning inference hardware based on in-memory computing remains a challenge. Here, the authors propose a strategy to train ResNet-type convolutional neural networks which results in reduced accuracy loss when transferring weights to in ...
Vinay Joshi, Manuel Le Gallo, Simon Haefeli, Irem Boybat, S. R. Nandakumar, Christophe Piveteau, Martino Dazzi, Bipin Rajendran, Abu Sebastian, Evangelos Eleftheriou   +9 more
doaj   +1 more source

Experimental Demonstration of In‐Memory Computing in a Ferrofluid System [PDF]

, 2023
Marco Crepaldi, Charanraj Mohan, Erik Garofalo, Andrew Adamatzky, Konrad Szaciłowski, Alessandro Chiolerio   +5 more
openalex   +1 more source

Nano device fabrication for in-memory and in-sensor reservoir computing

International Journal of Extreme Manufacturing
Recurrent neural networks (RNNs) have proven to be indispensable for processing sequential and temporal data, with extensive applications in language modeling, text generation, machine translation, and time-series forecasting.
Yinan Lin, Xi Chen, Qianyu Zhang, Junqi You, Renjing Xu, Zhongrui Wang, Linfeng Sun   +6 more
doaj   +1 more source

Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems

, 2018
A new class of non-homogeneous state-affine systems is introduced for use in reservoir computing. Sufficient conditions are identified that guarantee first, that the associated reservoir computers with linear readouts are causal, time-invariant, and ...
Grigoryeva, Lyudmila, Ortega, Juan-Pablo
core  

Physics-Based SPICE-Compatible Compact Model of FLASH Memory With Poly-Si Channel for Computing-in-Memory Applications

IEEE Journal of the Electron Devices Society
Recently, three-dimensional FLASH memory with multi-level cell characteristics has attracted increasing attention to enhance the capabilities of artificial intelligence (AI) by leveraging computingin-memory (CIM) systems.
Jung Rae Cho, Donghyun Ryu, Donguk Kim, Wonjung Kim, Yeonwoo Kim, Changwook Kim, Yoon Kim, Myounggon Kang, Jiyong Woo, Dae Hwan Kim   +9 more
doaj   +1 more source

In-Memory Distributed Matrix Computation Processing and Optimization

2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017
The use of large-scale machine learning and data mining methods is becoming ubiquitous in many application domains ranging from business intelligence and bioinformatics to self-driving cars. These methods heavily rely on matrix computations, and it is hence critical to make these computations scalable and efficient.
Yu, Yongyang, Tang, Mingjie, Aref, Walid G., Malluhi, Qutaibah M., Abbas, Mostafa M., Ouzzani, Mourad   +5 more
openaire   +2 more sources