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Mixed-precision in-memory computing [PDF]

Nature Electronics, 2018
As CMOS scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order to significantly extend the performance of today's computers. In-memory computing is a promising approach in which nanoscale resistive memory devices, organized in a ...
Manuel Le Gallo, Abu Sebastian, Roland Mathis, Matteo Manica, Heiner Giefers, Tomas Tuma, Costas Bekas, Alessandro Curioni, Evangelos Eleftheriou   +8 more
openaire   +2 more sources

Multistate resistive switching behaviors for neuromorphic computing in memristor

Materials Today Advances, 2021
Conventional Von Neumann computing systems encounter increasing challenges in the big-data era due to the constraints by the separated data storage and processing. Resistive random-access memory provides dual functionalities of data storage and computing
B. Sun, S. Ranjan, G. Zhou, T. Guo, Y. Xia, L. Wei, Y.N. Zhou, Y.A. Wu   +7 more
doaj   +1 more source

Floating Gate Transistor‐Based Accurate Digital In‐Memory Computing for Deep Neural Networks

Advanced Intelligent Systems, 2022
To improve the computing speed and energy efficiency of deep neural network (DNN) applications, in‐memory computing with nonvolatile memory (NVM) is proposed to address the time‐consuming and energy‐hungry data shuttling issue.
Runze Han, Peng Huang, Yachen Xiang, Hong Hu, Sheng Lin, Peiyan Dong, Wensheng Shen, Yanzhi Wang, Xiaoyan Liu, Jinfeng Kang   +9 more
doaj   +1 more source

Time Domain Analog Neuromorphic Engine Based on High-Density Non-Volatile Memory in Single-Poly CMOS

IEEE Access, 2022
Increasing the energy efficiency of deep learning systems is critical for improving the cognitive capability of edge devices, often battery operated, as well as for data centers, constrained by the total power envelope.
Tommaso Rizzo, Sebastiano Strangio, Giuseppe Iannaccone   +2 more
doaj   +1 more source

Graphene Oxide-Based Memristive Logic-in-Memory Circuit Enabling Normally-Off Computing

Nanomaterials, 2023
Memristive logic-in-memory circuits can provide energy- and cost-efficient computing, which is essential for artificial intelligence-based applications in the coming Internet-of-things era.
Yeongkwon Kim, Seung-Bae Jeon, Byung Chul Jang   +2 more
doaj   +1 more source

Architecture of Computing System based on Chiplet

Micromachines, 2022
Computing systems are widely used in medical diagnosis, climate prediction, autonomous vehicles, etc. As the key part of electronics, the performance of computing systems is crucial in the intellectualization of the equipment.
Guangbao Shan, Yanwen Zheng, Chaoyang Xing, Dongdong Chen, Guoliang Li, Yintang Yang   +5 more
doaj   +1 more source

An Efficient and Robust Partial Differential Equation Solver by Flash-Based Computing in Memory

Micromachines, 2023
Flash memory-based computing-in-memory (CIM) architectures have gained popularity due to their remarkable performance in various computation tasks of data processing, including machine learning, neuron networks, and scientific calculations. Especially in
Yueran Qi, Yang Feng, Jixuan Wu, Zhaohui Sun, Maoying Bai, Chengcheng Wang, Hai Wang, Xuepeng Zhan, Junyu Zhang, Jing Liu, Jiezhi Chen   +10 more
doaj   +1 more source

Parallel in-memory wireless computing

Nature Electronics, 2023
Parallel wireless digital communication with ultralow power consumption is critical for emerging edge technologies such as 5G and Internet of Things. However, the physical separation between digital computing units and analogue transmission units in traditional wireless technology leads to high power consumption.
Cong Wang, Gong-Jie Ruan, Zai-Zheng Yang, Xing-Jian Yangdong, Yixiang Li, Liang Wu, Yingmeng Ge, Yichen Zhao, Chen Pan, Wei Wei, Li-Bo Wang, Bin Cheng, Zaichen Zhang, Chuan Zhang, Shi-Jun Liang, Feng Miao   +15 more
openaire   +2 more sources

Multifunctional computing-in-memory SRAM cells based on two-surface-channel MoS2 transistors

iScience, 2021
Summary: Driven by technologies such as machine learning, artificial intelligence, and internet of things, the energy efficiency and throughput limitations of the von Neumann architecture are becoming more and more serious.
Fan Wang, Jiayi Li, Zhenhan Zhang, Yi Ding, Yan Xiong, Xiang Hou, Huawei Chen, Peng Zhou   +7 more
doaj   +1 more source

Truss Decomposition in Massive Networks [PDF]

, 2012
The k-truss is a type of cohesive subgraphs proposed recently for the study of networks. While the problem of computing most cohesive subgraphs is NP-hard, there exists a polynomial time algorithm for computing k-truss. Compared with k-core which is also
Cheng, James, Wang, Jia
core   +2 more sources