Search Results - Advanced Memory and Neural Computing

Filamentary TaOx/HfO2 ReRAM Devices for Neural Networks Training with Analog In‐Memory Computing by Stecconi, Tommaso, Guido, Roberto, Berchialla, Luca, La Porta, Antonio, Weiss, Jonas, Popoff, Youri, Halter, Mattia, Sousa, Marilyne, Horst, Folkert, Dávila, Diana, Drechsler, Ute, Dittmann, Regina, Offrein, Bert Jan, Bragaglia, Valeria

“…The in‐memory computing paradigm aims at overcoming the intrinsic inefficiencies of Von…”
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Floating Gate Transistor‐Based Accurate Digital In‐Memory Computing for Deep Neural Networks by Han, Runze, Huang, Peng, Xiang, Yachen, Hu, Hong, Lin, Sheng, Dong, Peiyan, Shen, Wensheng, Wang, Yanzhi, Liu, Xiaoyan, Kang, Jinfeng

“…To improve the computing speed and energy efficiency of deep neural network (DNN) applications, in‐memory computing with nonvolatile memory…”
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All‐Electrical Control of Spin Synapses for Neuromorphic Computing: Bridging Multi‐State Memory with Quantization for Efficient Neural Networks by Hsin, Tzu‐Chuan, Lin, Chun‐Yi, Wang, Po‐Chuan, Yang, Chun, Pai, Chi‐Feng

“…The development of energy‐efficient, brain‐inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to achieve high accuracy and adaptability…”
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Binarized Neural Network Comprising Quasi‐Nonvolatile Memory Devices for Neuromorphic Computing by Shin, Yunwoo, Jeon, Juhee, Cho, Kyoungah, Kim, Sangsig

“…This study presents a binarized neural network (BNN) comprising quasi‐nonvolatile memory (QNVM) devices that operate in a positive feedback loop mechanism and exhibit an extremely low subthreshold swing…”
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Device Variation Effects on Neural Network Inference Accuracy in Analog In‐Memory Computing Systems by Wang, Qiwen, Park, Yongmo, Lu, Wei D.

“…In analog in‐memory computing systems based on nonvolatile memories such as resistive random‐access memory (RRAM…”
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Filamentary TaO x /HfO 2 ReRAM Devices for Neural Networks Training with Analog In‐Memory Computing by Stecconi, Tommaso, Guido, Roberto, Berchialla, Luca, La Porta, Antonio, Weiss, Jonas, Popoff, Youri, Halter, Mattia, Sousa, Marilyne, Horst, Folkert, Dávila, Diana, Drechsler, Ute, Dittmann, Regina, Offrein, Bert Jan, Bragaglia, Valeria

“…The in‐memory computing paradigm aims at overcoming the intrinsic inefficiencies of Von…”
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Memory level neural network: A time-varying neural network for memory input processing by Gong, Chao, Zhou, Xianwei, Lü, Xing, Lin, Fuhong

“… However, the instantaneous and memory fusion input characteristic makes current neural networks not suitable for affective computing…”
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Layer ensemble averaging for fault tolerance in memristive neural networks by Yousuf, Osama, Hoskins, Brian D., Ramu, Karthick, Fream, Mitchell, Borders, William A., Madhavan, Advait, Daniels, Matthew W., Dienstfrey, Andrew, McClelland, Jabez J., Lueker-Boden, Martin, Adam, Gina C.

“…Artificial neural networks have advanced due to scaling dimensions, but conventional computing struggles with inefficiencies due to memory bottlenecks…”
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High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network by Zhang, Kun, Jia, Xiaotao, Cao, Kaihua, Wang, Jinkai, Zhang, Yue, Lin, Kelian, Chen, Lei, Feng, Xueqiang, Zheng, Zhenyi, Zhang, Zhizhong, Zhang, Youguang, Zhao, Weisheng

“…‐level operation and low on/off ratio, greatly hinder their further application for advanced computing concepts, such as deep neural network (DNN) accelerator…”
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Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers by Achite, Mohammed, Katipoğlu, Okan Mert, Kartal, Veysi, Sarıgöl, Metin, Jehanzaib, Muhammad, Gül, Enes

“… In this study, advanced soft computing techniques, including long short-term memory (LSTM), convolutional neural network…”
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MulPi: A Multi-class and Patient-Independent Epileptic Seizure Classifier With Co-Designed Input-stationary Computing-in-SRAM by Kim, Bokyung, Huang, Qijia, Taylor, Brady, Zheng, Qilin, Ku, Jonathan, Chen, Yiran, Li, Hai

“… We develop a 5-layer convolutional neural network (CNN), MulPiCNN , with advanced training techniques for lightness and accuracy…”
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Modulating Neuromorphic Behavior of Organic Synaptic Electrolyte-Gated Transistors Through Microstructure Engineering and Potential Applications by Wu, Fu-Chiao, Chen, Chun-Yu, Wang, Yu-Wu, You, Chun-Bin, Wang, Li-Yun, Ruan, Jrjeng, Chou, Wei-Yang, Lai, Wei-Chih, Cheng, Horng-Long

“…Organic synaptic transistors are a promising technology for advanced electronic devices with simultaneous computing and memory functions and for the application of artificial neural networks…”
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Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing by Mehonic, Adnan, Sebastian, Abu, Rajendran, Bipin, Simeone, Osvaldo, Vasilaki, Eleni, Kenyon, Anthony J.

“…). DL is based on computational models that are, to a certain extent, bio‐inspired, as they rely on networks of connected simple computing units operating in parallel…”
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Hybrid computing using a neural network with dynamic external memory by Graves, Alex, Wayne, Greg, Reynolds, Malcolm, Harley, Tim, Danihelka, Ivo, Grabska-Barwińska, Agnieszka, Colmenarejo, Sergio Gómez, Grefenstette, Edward, Ramalho, Tiago, Agapiou, John, Badia, Adrià Puigdomènech, Hermann, Karl Moritz, Zwols, Yori, Ostrovski, Georg, Cain, Adam, King, Helen, Summerfield, Christopher, Blunsom, Phil, Kavukcuoglu, Koray, Hassabis, Demis

“… and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC…”
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Brain-inspired computing with resistive switching memory (RRAM): Devices, synapses and neural networks by Ielmini, Daniele

“…The human brain can perform advanced computing tasks, such as learning, recognition, and cognition, with extremely low power consumption and low frequency of neuronal spiking…”
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A Microscaling Multi-Mode Gain-Cell Computing-in-Memory Macro for Advanced AI Edge Device by Tien, Jen-Chun, Wu, Ping-Chun, Khwa, Win-San, Sanjay Lele, Ashwin, Su, Jian-Wei, Cheng, Chiao-Yen, Hsu, Jun-Ming, Chen, Yu-Chen, Hsieh, Le-Jung, Bai, Jyun-Cheng, Kao, Yu-Sheng, Lou, Tsung-Han, Wu, Jui-Jen, Lo, Chung-Chuan, Liu, Ren-Shuo, Hsieh, Chih-Cheng, Tang, Kea-Tiong, Chang, Meng-Fan

“…) values into low-bitwidth FP-like values with a shared-scale (SS) exponent. When implemented with computing-in-memory (CIM…”
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Neural Architecture Search with In‐Memory Multiply–Accumulate and In‐Memory Rank Based on Coating Layer Optimized C‐Doped Ge2Sb2Te5 Phase Change Memory by Yan, Longhao, Wu, Qingyu, Li, Xi, Xie, Chenchen, Zhou, Xilin, Li, Yuqi, Shi, Daijing, Yu, Lianfeng, Zhang, Teng, Tao, Yaoyu, Yan, Bonan, Zhong, Min, Song, Zhitang, Yang, Yuchao, Huang, Ru

“… Herein, 4 Mb phase change memory (PCM) chips are first fabricated that enable two key in‐memory computing operations—in‐memory multiply‐accumulate (MAC…”
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Smart load balancing in cloud computing: Integrating feature selection with advanced deep learning models by Sanjalawe, Yousef, Fraihat, Salam, Al-E’mari, Salam, Abualhaj, Mosleh, Makhadmeh, Sharif, Alzubi, Emran

“…The increasing dependence on cloud computing as a cornerstone of modern technological infrastructures has introduced significant challenges in resource management…”
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XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks by Yin, Shihui, Jiang, Zhewei, Seo, Jae-Sun, Seok, Mingoo

“…We present XNOR-SRAM, a mixed-signal in-memory computing (IMC) SRAM macro that computes ternary-XNOR-and-accumulate (XAC…”
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Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing by Wang, Shuiyuan, Liu, Lan, Gan, Lurong, Chen, Huawei, Hou, Xiang, Ding, Yi, Ma, Shunli, Zhang, David Wei, Zhou, Peng

“…With the advent of the big data era, applications are more data-centric and energy efficiency issues caused by frequent data interactions, due to the physical separation of memory and computing, will become increasingly severe…”
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