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  1. 1

    Trapezoid: A Versatile Accelerator for Dense and Sparse Matrix Multiplications by Yang, Yifan, Emer, Joel S., Sanchez, Daniel

    Published: IEEE 29.06.2024
    “…Accelerating matrix multiplication is crucial to achieve high performance in many application domains, including neural networks, graph analytics, and…”
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    Conference Proceeding
  2. 2

    Numerically-Stable and Highly-Scalable Parallel LU Factorization for Circuit Simulation by Chen, Xiaoming

    ISSN: 1558-2434
    Published: ACM 29.10.2022
    “…A number of sparse linear systems are solved by sparse LU factorization in a circuit simulation process. The coefficient matrices of these linear systems have…”
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    Conference Proceeding
  3. 3

    Me-MPK: Accelerating Krylov Subspace Solvers via Memory-efficient Matrix-Power Kernel by Qiu, Haozhong, Xu, Chuanfu, Fang, Jianbin, Li, Shengguo, Deng, Liang, Zhang, Jian, Dai, Zhe, Ding, Yue, Wang, Yue, Han, Zhimeng, Che, Yonggang, Liu, Jie

    Published: IEEE 22.06.2025
    “…This paper focuses on optimizing the Matrix-Power Kernel (MPK), which relies on a series of Sparse Matrix-Vector multiplications (SpMVs) using the same sparse…”
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    Conference Proceeding
  4. 4

    An Input-Aware Sparse Tensor Compiler Empowered by Vectorized Acceleration by He, Xianhao, Wang, Haotian, Zhang, Jiapeng, Yang, Wangdong, Chronopoulos, Anthony Theodore, Li, Kenli

    Published: IEEE 22.06.2025
    “…Sparsity is widely prevalent in real-world applications, yet existing compiler optimizations and code generation techniques for sparse computations remain…”
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    Conference Proceeding
  5. 5

    InnerSP: A Memory Efficient Sparse Matrix Multiplication Accelerator with Locality-Aware Inner Product Processing by Baek, Daehyeon, Hwang, Soojin, Heo, Taekyung, Kim, Daehoon, Huh, Jaehyuk

    Published: IEEE 01.09.2021
    “…Sparse matrix multiplication is one of the key computational kernels in large-scale data analytics. However, a naive implementation suffers from the overheads…”
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  6. 6

    Tackling the Matrix Multiplication Micro-Kernel Generation with Exo by Castello, Adrian, Bellavita, Julian, Dinh, Grace, Ikarashi, Yuka, Martinez, Hector

    ISSN: 2643-2838
    Published: IEEE 02.03.2024
    “…The optimization of the matrix multiplication (or GEMM) has been a need during the last decades. This operation is considered the flagship of current linear…”
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    Conference Proceeding
  7. 7

    SSpMV: A Sparsity-aware SpMV Framework Empowered by Multimodal Machine Learning by Lin, Shengle, Liu, Chubo, Ding, Yan, Zhou, Joey Tianyi, Li, Kenli, Yang, Wangdong

    Published: IEEE 22.06.2025
    “…Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse operation in scientific computing and artificial intelligence. Efficiently adapting SpMV…”
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    Conference Proceeding
  8. 8

    ReSMiPS: A ReRAM-based Sparse Mixed-precision Solver with Fast Matrix Reordering Algorithm by Fu, Yuyang, Li, Jiancong, Chen, Jia, Zhou, Zhiwei, Zhou, Houji, Peng, Wenlong, Li, Yi, Miao, Xiangshui

    Published: IEEE 22.06.2025
    “…The solution of sparse matrix equations is essential in scientific computing. However, traditional solvers on digital computing platforms are limited by memory…”
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    Conference Proceeding
  9. 9

    Pipirima: Predicting Patterns in Sparsity to Accelerate Matrix Algebra by Bakhtiar, Ubaid, Joo, Donghyeon, Asgari, Bahar

    Published: IEEE 22.06.2025
    “…While sparsity, a feature of data in many applications, provides optimization opportunities such as reducing unnecessary computations, data transfers, and…”
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    Conference Proceeding
  10. 10

    FSPA: An FeFET-based Sparse Matrix-Dense Vector Multiplication Accelerator by Zhang, Xiaoyu, Li, Zerun, Liu, Rui, Chen, Xiaoming, Han, Yinhe

    Published: IEEE 09.07.2023
    “…Sparse matrix-dense vector multiplication (SpMV) is widely used in various applications. The performance of traditional SpMV accelerators is bounded by memory…”
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    Conference Proceeding
  11. 11

    JITSPMM: Just-in-Time Instruction Generation for Accelerated Sparse Matrix-Matrix Multiplication by Fu, Qiang, Rolinger, Thomas B., Huang, H. Howie

    ISSN: 2643-2838
    Published: IEEE 02.03.2024
    “…Achieving high performance for Sparse Matrix-Matrix Multiplication (SpMM) has received increasing research attention, especially on multi-core CPUs, due to the…”
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  12. 12

    pSyncPIM: Partially Synchronous Execution of Sparse Matrix Operations for All-Bank PIM Architectures by Baek, Daehyeon, Hwang, Soojin, Huh, Jaehyuk

    Published: IEEE 29.06.2024
    “…Recent commercial incarnations of processing-in-memory (PIM) maintain the standard DRAM interface and employ the all-bank mode execution to maximize bank-level…”
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    Conference Proceeding
  13. 13

    SpV8: Pursuing Optimal Vectorization and Regular Computation Pattern in SpMV by Li, Chenyang, Xia, Tian, Zhao, Wenzhe, Zheng, Nanning, Ren, Pengju

    Published: IEEE 05.12.2021
    “…Sparse Matrix-Vector Multiplication (SpMV) plays an important role in many scientific and industry applications, and remains a well-known challenge due to the…”
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    Conference Proceeding
  14. 14

    Accurate Reliability Evaluation and Enhancement via Probabilistic Transfer Matrices by Krishnaswamy, Smita, Viamontes, George F., Markov, Igor L., Hayes, John P.

    ISBN: 9780769522883, 0769522882
    ISSN: 1530-1591
    Published: Washington, DC, USA IEEE Computer Society 07.03.2005
    Published in Design, Automation and Test in Europe (07.03.2005)
    “…Soft errors are an increasingly serious problem for logic circuits. To estimate the effects of soft errors on such circuits, we develop a general computational…”
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    Conference Proceeding
  15. 15

    Red-Blue Pebbling Revisited: Near Optimal Parallel Matrix-Matrix Multiplication by Kwasniewski, Grzegorz, Kabic, Marko, Besta, Maciej, VandeVondele, Joost, Solca, Raffaele, Hoefler, Torsten

    ISSN: 2167-4337
    Published: ACM 17.11.2019
    “…We propose COSMA: a parallel matrix-matrix multiplication algorithm that is near communication-optimal for all combinations of matrix dimensions, processor…”
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    Conference Proceeding
  16. 16

    SLATE: Design of a Modern Distributed and Accelerated Linear Algebra Library by Gates, Mark, Kurzak, Jakub, Charara, Ali, YarKhan, Asim, Dongarra, Jack

    ISSN: 2167-4337
    Published: ACM 17.11.2019
    “…The SLATE (Software for Linear Algebra Targeting Exascale) library is being developed to provide fundamental dense linear algebra capabilities for current and…”
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    Conference Proceeding
  17. 17

    A Methodology for Characterizing Sparse Datasets and Its Application to SIMD Performance Prediction by Zhu, Gangyi, Jiang, Peng, Agrawal, Gagan

    ISSN: 2641-7936
    Published: IEEE 01.09.2019
    “…Irregular computations are commonly seen in many scientific and engineering domains that use unstructured meshes or sparse matrices. The performance of an…”
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    Conference Proceeding
  18. 18

    A tensor-based volterra series black-box nonlinear system identification and simulation framework by Batselier, Kim, Zhongming Chen, Haotian Liu, Ngai Wong

    ISSN: 1558-2434
    Published: ACM 01.11.2016
    “…Tensors are a multi-linear generalization of matrices to their d-way counterparts, and are receiving intense interest recently due to their natural…”
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  19. 19

    Solvability of Matrix-Exponential Equations by Ouaknine, Joel, Pouly, Amaury, Sousa-Pinto, Joao, Worrell, James

    ISBN: 9781450343916, 1450343910
    Published: New York, NY, USA ACM 05.07.2016
    “…We consider a continuous analogue of (Babai et al. 1996)'s and (Cai et al. 2000)'s problem of solving multiplicative matrix equations. Given k + 1 square…”
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    Conference Proceeding
  20. 20

    Steady-state dynamic temperature analysis and reliability optimization for embedded multiprocessor systems by Ukhov, Ivan, Bao, Min, Eles, Petru, Peng, Zebo

    ISBN: 1450311997, 9781450311991
    ISSN: 0738-100X
    Published: New York, NY, USA ACM 03.06.2012
    Published in DAC Design Automation Conference 2012 (03.06.2012)
    “…In this paper we propose an analytical technique for the steady-state dynamic temperature analysis (SSDTA) of multiprocessor systems with periodic…”
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