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RRAM-based parallel computing architecture using k-nearest neighbor classification for pattern recognition. [PDF]

Sci Rep, 2017
Resistive switching memory (RRAM) is considered as one of the most promising devices for parallel computing solutions that may overcome the von Neumann bottleneck of today’s electronic systems.
Jiang Y, Kang J, Wang X.
europepmc   +2 more sources

Wukong: a scalable and locality-enhanced framework for serverless parallel computing [PDF]

ACM Symposium on Cloud Computing, 2020
Executing complex, burst-parallel, directed acyclic graph (DAG) jobs poses a major challenge for serverless execution frameworks, which will need to rapidly scale and schedule tasks at high throughput, while minimizing data movement across tasks.
Benjamin Carver, Jingyuan Zhang, Ao Wang, Ali Anwar, Panruo Wu, Yue Cheng   +5 more
semanticscholar   +1 more source

Improving Structured Grid-Based Sparse Matrix-Vector Multiplication and Gauss–Seidel Iteration on GPDSP

Applied Sciences, 2023
Structured grid-based sparse matrix-vector multiplication and Gauss–Seidel iterations are very important kernel functions in scientific and engineering computations, both of which are memory intensive and bandwidth-limited.
Yang Wang, Jie Liu, Xiaoxiong Zhu, Qingyang Zhang, Shengguo Li, Qinglin Wang   +5 more
doaj   +1 more source

Device-Simulation-Based Machine Learning Technique for the Characteristic of Line Tunnel Field-Effect Transistors

IEEE Access, 2022
With the rapid growth of the semiconductor manufacturing industry, it has been evident that device simulation has been considered a sluggish process.
Chandni Akbar, Yiming Li, Narasimhulu Thoti   +2 more
doaj   +1 more source

Parallel computing

Future generations computer systems, 2020
ion in parallel application developmnet (Covered at Lecture 4)
Mohamed Zahran
semanticscholar   +3 more sources

Electronic Structures of Monolayer Binary and Ternary 2D Materials: MoS₂, WS₂, Mo_1−xCr_xS₂, and W_1−xCr_xS₂ Using Density Functional Theory Calculations

Nanomaterials, 2022
Two-dimensional (2D) materials with binary compounds, such as transition-metal chalcogenides, have emerged as complementary materials due to their tunable band gap and modulated electrical properties via the layer number.
Chieh-Yang Chen, Yiming Li, Min-Hui Chuang   +2 more
doaj   +1 more source

Effects of Random Nanosized TiN Grain on Characteristic of Gate-All-Around FinFETs with Ferroelectric HZO Layer

Nanoscale Research Letters, 2022
In this paper, we computationally study electrical characteristics for gate-all-around fin field effect transistors (GAA FinFETs) and negative capacitance GAA FinFETs (NC-GAA FinFETs) for sub-3-nm technological nodes.
Yiming Li, Min-Hui Chuang, Yu-Chin Tsai
doaj   +1 more source

A Machine Learning Approach to Modeling Intrinsic Parameter Fluctuation of Gate-All-Around Si Nanosheet MOSFETs

IEEE Access, 2022
The sensitivity of semiconductor devices to any microscopic perturbation is increasing with the continuous shrinking of device technology. Even the small fluctuations have become more acute for highly scaled nano-devices.
Rajat Butola, Yiming Li, Sekhar Reddy Kola   +2 more
doaj   +1 more source

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

IEEE Access, 2021
Device simulation has been explored and industrialized for over 40 years; however, it still requires huge computational cost. Therefore, it can be further advanced using deep learning (DL) algorithms.
Chandni Akbar, Yiming Li, Wen-Li Sung
doaj   +1 more source

A Hybrid 1D-CNN-LSTM Technique for WKF-Induced Variability of Multi-Channel GAA NS- and NF-FETs

IEEE Access, 2023
Presently deep learning (DL) techniques are massively used in the semiconductor industry. At the same time, applying a deep learning approach for small datasets is also an immense challenge as larger dataset generation needs more computational time-cost ...
Sagarika Dash, Yiming Li, Wen-Li Sung
doaj   +1 more source