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Статті в журналах з теми "In-memory-computing (IMC)"
Song, Soonbum, and Youngmin Kim. "Novel In-Memory Computing Adder Using 8+T SRAM." Electronics 11, no. 6 (March 16, 2022): 929. http://dx.doi.org/10.3390/electronics11060929.
Повний текст джерелаMannocci, P., M. Farronato, N. Lepri, L. Cattaneo, A. Glukhov, Z. Sun, and D. Ielmini. "In-memory computing with emerging memory devices: Status and outlook." APL Machine Learning 1, no. 1 (March 1, 2023): 010902. http://dx.doi.org/10.1063/5.0136403.
Повний текст джерелаSun, Zhaohui, Yang Feng, Peng Guo, Zheng Dong, Junyu Zhang, Jing Liu, Xuepeng Zhan, Jixuan Wu, and Jiezhi Chen. "Flash-based in-memory computing for stochastic computing in image edge detection." Journal of Semiconductors 44, no. 5 (May 1, 2023): 054101. http://dx.doi.org/10.1088/1674-4926/44/5/054101.
Повний текст джерелаPedretti, Giacomo, and Daniele Ielmini. "In-Memory Computing with Resistive Memory Circuits: Status and Outlook." Electronics 10, no. 9 (April 30, 2021): 1063. http://dx.doi.org/10.3390/electronics10091063.
Повний текст джерелаBansla, Neetu, and Rajneesh . "Future ERP: In-Memory Computing (IMC)Technology Infusion." Journal of Information Technology and Sciences 6, no. 3 (October 26, 2020): 17–21. http://dx.doi.org/10.46610/joits.2020.v06i03.003.
Повний текст джерелаKim, Manho, Sung-Ho Kim, Hyuk-Jae Lee, and Chae-Eun Rhee. "Case Study on Integrated Architecture for In-Memory and In-Storage Computing." Electronics 10, no. 15 (July 21, 2021): 1750. http://dx.doi.org/10.3390/electronics10151750.
Повний текст джерелаZhang, Jin, Zhiting Lin, Xiulong Wu, Chunyu Peng, Wenjuan Lu, Qiang Zhao, and Junning Chen. "An 8T SRAM Array with Configurable Word Lines for In-Memory Computing Operation." Electronics 10, no. 3 (January 27, 2021): 300. http://dx.doi.org/10.3390/electronics10030300.
Повний текст джерелаXue, Wang, Liu, Lv, Wang, and Zeng. "An RISC-V Processor with Area-Efficient Memristor-Based In-Memory Computing for Hash Algorithm in Blockchain Applications." Micromachines 10, no. 8 (August 16, 2019): 541. http://dx.doi.org/10.3390/mi10080541.
Повний текст джерелаKrishnan, Gokul, Sumit K. Mandal, Manvitha Pannala, Chaitali Chakrabarti, Jae-Sun Seo, Umit Y. Ogras, and Yu Cao. "SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks." ACM Transactions on Embedded Computing Systems 20, no. 5s (October 31, 2021): 1–24. http://dx.doi.org/10.1145/3476999.
Повний текст джерелаKiran Cherupally, Sai, Jian Meng, Adnan Siraj Rakin, Shihui Yin, Injune Yeo, Shimeng Yu, Deliang Fan, and 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, no. 3 (January 13, 2022): 034001. http://dx.doi.org/10.1088/1361-6641/ac461f.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаWith 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
Тези доповідей конференцій з теми "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." In 2018 IEEE International Memory Workshop (IMW). IEEE, 2018. http://dx.doi.org/10.1109/imw.2018.8388838.
Повний текст джерелаMayahinia, Mahta, Christopher Munch, and Mehdi B. Tahoori. "Analyzing and Mitigating Sensing Failures in Spintronic-based Computing in Memory." In 2021 IEEE International Test Conference (ITC). IEEE, 2021. http://dx.doi.org/10.1109/itc50571.2021.00036.
Повний текст джерела"Invited Talk #9: Ferroelectric Capacitive Memory for Storage and In-memory Computing." In 2022 International Conference on IC Design and Technology (ICICDT). IEEE, 2022. http://dx.doi.org/10.1109/icicdt56182.2022.9933082.
Повний текст джерелаLuo, Yuan-Chun, Anni Lu, Jae Hur, Shaolan Li, and Shimeng Yu. "Design of Non-volatile Capacitive Crossbar Array for In-Memory Computing." In 2021 IEEE International Memory Workshop (IMW). IEEE, 2021. http://dx.doi.org/10.1109/imw51353.2021.9439603.
Повний текст джерелаLin, Yu-Hsuan, Po-Hao Tseng, Feng-Min Lee, Ming-Hsiu Lee, Chih-Chang Hsieh, Dai-Ying Lee, Keh-Chung Wang, and Chih-Yuan Lu. "NOR Flash-based Multilevel In-Memory-Searching Architecture for Approximate Computing." In 2022 IEEE International Memory Workshop (IMW). IEEE, 2022. http://dx.doi.org/10.1109/imw52921.2022.9779250.
Повний текст джерелаYoda, Hiroaki, Yuichi Ohsawa, Yushi Kato, and Tomomi Yoda. "Proposal of A Nonvolatile XNOR Logic-Gate Using Voltage-Control Spintronics Memory Cells For In-Memory Computing." In 2020 IEEE International Memory Workshop (IMW). IEEE, 2020. http://dx.doi.org/10.1109/imw48823.2020.9108120.
Повний текст джерелаHalawani, Yasmin, Baker Mohammad, Mahmoud Al-Qutayri, and Said Al-Sarawi. "A Re-configurable Memristor Array Structure for In-Memory Computing Applications." In 2018 30th International Conference on Microelectronics (ICM). IEEE, 2018. http://dx.doi.org/10.1109/icm.2018.8704111.
Повний текст джерелаNishimura, Takuto, Yuya Ichikawa, Akira Goda, Naoko Misawa, Chihiro Matsui, and Ken Takeuchi. "Stochastic Computing-based Computation-in-Memory (SC CiM) Architecture for DNNs and Hierarchical Evaluations of Non-volatile Memory Error and Defect Tolerance." In 2023 IEEE International Memory Workshop (IMW). IEEE, 2023. http://dx.doi.org/10.1109/imw56887.2023.10145982.
Повний текст джерелаZiegler, Tobias, Leon Brackmann, Tyler Hennen, Christopher Bengel, Stephan Menzel, and Dirk J. Wouters. "Eliminating Capacitive Sneak Paths in Associative Capacitive Networks based on Complementary Resistive Switches for In-Memory Computing." In 2023 IEEE International Memory Workshop (IMW). IEEE, 2023. http://dx.doi.org/10.1109/imw56887.2023.10145947.
Повний текст джерелаHsu, Po-Kai, and Shimeng Yu. "In-Memory 3D NAND Flash Hyperdimensional Computing Engine for Energy-Efficient SARS-CoV-2 Genome Sequencing." In 2022 IEEE International Memory Workshop (IMW). IEEE, 2022. http://dx.doi.org/10.1109/imw52921.2022.9779291.
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