Literatura académica sobre el tema "In-memory-computing (IMC)"
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Artículos de revistas sobre el tema "In-memory-computing (IMC)"
Song, Soonbum y Youngmin Kim. "Novel In-Memory Computing Adder Using 8+T SRAM". Electronics 11, n.º 6 (16 de marzo de 2022): 929. http://dx.doi.org/10.3390/electronics11060929.
Texto completoMannocci, P., M. Farronato, N. Lepri, L. Cattaneo, A. Glukhov, Z. Sun y D. Ielmini. "In-memory computing with emerging memory devices: Status and outlook". APL Machine Learning 1, n.º 1 (1 de marzo de 2023): 010902. http://dx.doi.org/10.1063/5.0136403.
Texto completoSun, Zhaohui, Yang Feng, Peng Guo, Zheng Dong, Junyu Zhang, Jing Liu, Xuepeng Zhan, Jixuan Wu y Jiezhi Chen. "Flash-based in-memory computing for stochastic computing in image edge detection". Journal of Semiconductors 44, n.º 5 (1 de mayo de 2023): 054101. http://dx.doi.org/10.1088/1674-4926/44/5/054101.
Texto completoPedretti, Giacomo y Daniele Ielmini. "In-Memory Computing with Resistive Memory Circuits: Status and Outlook". Electronics 10, n.º 9 (30 de abril de 2021): 1063. http://dx.doi.org/10.3390/electronics10091063.
Texto completoBansla, Neetu y Rajneesh . "Future ERP: In-Memory Computing (IMC)Technology Infusion". Journal of Information Technology and Sciences 6, n.º 3 (26 de octubre de 2020): 17–21. http://dx.doi.org/10.46610/joits.2020.v06i03.003.
Texto completoKim, Manho, Sung-Ho Kim, Hyuk-Jae Lee y Chae-Eun Rhee. "Case Study on Integrated Architecture for In-Memory and In-Storage Computing". Electronics 10, n.º 15 (21 de julio de 2021): 1750. http://dx.doi.org/10.3390/electronics10151750.
Texto completoZhang, Jin, Zhiting Lin, Xiulong Wu, Chunyu Peng, Wenjuan Lu, Qiang Zhao y Junning Chen. "An 8T SRAM Array with Configurable Word Lines for In-Memory Computing Operation". Electronics 10, n.º 3 (27 de enero de 2021): 300. http://dx.doi.org/10.3390/electronics10030300.
Texto completoXue, Wang, Liu, Lv, Wang y Zeng. "An RISC-V Processor with Area-Efficient Memristor-Based In-Memory Computing for Hash Algorithm in Blockchain Applications". Micromachines 10, n.º 8 (16 de agosto de 2019): 541. http://dx.doi.org/10.3390/mi10080541.
Texto completoKrishnan, Gokul, Sumit K. Mandal, Manvitha Pannala, Chaitali Chakrabarti, Jae-Sun Seo, Umit Y. Ogras y Yu Cao. "SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks". ACM Transactions on Embedded Computing Systems 20, n.º 5s (31 de octubre de 2021): 1–24. http://dx.doi.org/10.1145/3476999.
Texto completoKiran Cherupally, Sai, Jian Meng, Adnan Siraj Rakin, Shihui Yin, Injune Yeo, Shimeng Yu, Deliang Fan y Jae-Sun Seo. "Improving the accuracy and robustness of RRAM-based in-memory computing against RRAM hardware noise and adversarial attacks". Semiconductor Science and Technology 37, n.º 3 (13 de enero de 2022): 034001. http://dx.doi.org/10.1088/1361-6641/ac461f.
Texto completoTesis sobre el tema "In-memory-computing (IMC)"
Ezzadeen, Mona. "Conception d'un circuit dédié au calcul dans la mémoire à base de technologie 3D innovante". Electronic Thesis or Diss., Aix-Marseille, 2022. http://theses.univ-amu.fr.lama.univ-amu.fr/221212_EZZADEEN_955e754k888gvxorp699jljcho_TH.pdf.
Texto completoWith the advent of edge devices and artificial intelligence, the data deluge is a reality, making energy-efficient computing systems a must-have. Unfortunately, classical von Neumann architectures suffer from the high cost of data transfers between memories and processing units. At the same time, CMOS scaling seems more and more challenging and costly to afford, limiting the chips' performance due to power consumption issues.In this context, bringing the computation directly inside or near memories (I/NMC) seems an appealing solution. However, data-centric applications require an important amount of non-volatile storage, and modern Flash memories suffer from scaling issues and are not very suited for I/NMC. On the other hand, emerging memory technologies such as ReRAM present very appealing memory performances, good scalability, and interesting I/NMC features. However, they suffer from variability issues and from a degraded density integration if an access transistor per bitcell (1T1R) is used to limit the sneak-path currents. This thesis work aims to overcome these two challenges. First, the variability impact on read and I/NMC operations is assessed and new robust and low-overhead ReRAM-based boolean operations are proposed. In the context of neural networks, new ReRAM-based neuromorphic accelerators are developed and characterized, with an emphasis on good robustness against variability, good parallelism, and high energy efficiency. Second, to resolve the density integration issues, an ultra-dense 3D 1T1R ReRAM-based Cube and its architecture are proposed, which can be used as a 3D NOR memory as well as a low overhead and energy-efficient I/NMC accelerator
Actas de conferencias sobre el tema "In-memory-computing (IMC)"
Li, Can, Daniel Belkin, Yunning Li, Peng Yan, Miao Hu, Ning Ge, Hao Jiang et al. "In-Memory Computing with Memristor Arrays". En 2018 IEEE International Memory Workshop (IMW). IEEE, 2018. http://dx.doi.org/10.1109/imw.2018.8388838.
Texto completoMayahinia, Mahta, Christopher Munch y Mehdi B. Tahoori. "Analyzing and Mitigating Sensing Failures in Spintronic-based Computing in Memory". En 2021 IEEE International Test Conference (ITC). IEEE, 2021. http://dx.doi.org/10.1109/itc50571.2021.00036.
Texto completo"Invited Talk #9: Ferroelectric Capacitive Memory for Storage and In-memory Computing". En 2022 International Conference on IC Design and Technology (ICICDT). IEEE, 2022. http://dx.doi.org/10.1109/icicdt56182.2022.9933082.
Texto completoLuo, Yuan-Chun, Anni Lu, Jae Hur, Shaolan Li y Shimeng Yu. "Design of Non-volatile Capacitive Crossbar Array for In-Memory Computing". En 2021 IEEE International Memory Workshop (IMW). IEEE, 2021. http://dx.doi.org/10.1109/imw51353.2021.9439603.
Texto completoLin, Yu-Hsuan, Po-Hao Tseng, Feng-Min Lee, Ming-Hsiu Lee, Chih-Chang Hsieh, Dai-Ying Lee, Keh-Chung Wang y Chih-Yuan Lu. "NOR Flash-based Multilevel In-Memory-Searching Architecture for Approximate Computing". En 2022 IEEE International Memory Workshop (IMW). IEEE, 2022. http://dx.doi.org/10.1109/imw52921.2022.9779250.
Texto completoYoda, Hiroaki, Yuichi Ohsawa, Yushi Kato y Tomomi Yoda. "Proposal of A Nonvolatile XNOR Logic-Gate Using Voltage-Control Spintronics Memory Cells For In-Memory Computing". En 2020 IEEE International Memory Workshop (IMW). IEEE, 2020. http://dx.doi.org/10.1109/imw48823.2020.9108120.
Texto completoHalawani, Yasmin, Baker Mohammad, Mahmoud Al-Qutayri y Said Al-Sarawi. "A Re-configurable Memristor Array Structure for In-Memory Computing Applications". En 2018 30th International Conference on Microelectronics (ICM). IEEE, 2018. http://dx.doi.org/10.1109/icm.2018.8704111.
Texto completoNishimura, Takuto, Yuya Ichikawa, Akira Goda, Naoko Misawa, Chihiro Matsui y Ken Takeuchi. "Stochastic Computing-based Computation-in-Memory (SC CiM) Architecture for DNNs and Hierarchical Evaluations of Non-volatile Memory Error and Defect Tolerance". En 2023 IEEE International Memory Workshop (IMW). IEEE, 2023. http://dx.doi.org/10.1109/imw56887.2023.10145982.
Texto completoZiegler, Tobias, Leon Brackmann, Tyler Hennen, Christopher Bengel, Stephan Menzel y Dirk J. Wouters. "Eliminating Capacitive Sneak Paths in Associative Capacitive Networks based on Complementary Resistive Switches for In-Memory Computing". En 2023 IEEE International Memory Workshop (IMW). IEEE, 2023. http://dx.doi.org/10.1109/imw56887.2023.10145947.
Texto completoHsu, Po-Kai y Shimeng Yu. "In-Memory 3D NAND Flash Hyperdimensional Computing Engine for Energy-Efficient SARS-CoV-2 Genome Sequencing". En 2022 IEEE International Memory Workshop (IMW). IEEE, 2022. http://dx.doi.org/10.1109/imw52921.2022.9779291.
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