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Platinum: Path-Adaptable LUT-Based Accelerator Tailored for Low-Bit Weight Matrix Multiplication [PDF]

open access: green
Shan, Haoxuan   +7 more
openalex  

TileSpGEMM: a tiled algorithm for parallel sparse general matrix-matrix multiplication on GPUs

ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming, 2022
Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental building blocks in sparse linear solvers, graph processing frameworks and machine learning applications.
Yuyao Niu   +5 more
semanticscholar   +1 more source

On Matrix Multiplication with Homomorphic Encryption

Cloud Computing Security Workshop, 2022
Homomorphic Encryption (HE) is one of the main cryptographic tools used to enable secure computation outsourcing. Data is outsourced encrypted to an untrusted service provider and remain encrypted during processing. In the last decade, the performance of
P. Rizomiliotis, Aikaterini Triakosia
semanticscholar   +1 more source

Threaded Accurate Matrix-Matrix Multiplications with Sparse Matrix-Vector Multiplications

2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2018
Basic Linear Algebra Subprograms (BLAS) is a frequently used numerical library for linear algebra computations. However, it places little emphasis on computational accuracy, especially with respect to the accuracy assurance of the results. Although some algorithms for ensuring the computational accuracy of BLAS operations have been studied, there is a ...
Shuntaro Ichimura   +4 more
openaire   +1 more source

Coded Computing for Resilient, Secure, and Privacy-Preserving Distributed Matrix Multiplication

IEEE Transactions on Communications, 2021
Coded computing is a new framework to address fundamental issues in large scale distributed computing, by injecting structured randomness and redundancy. We first provide an overview of coded computing and summarize some recent advances. Then we focus on
Qian Yu, A. Avestimehr
semanticscholar   +1 more source

MatRaptor: A Sparse-Sparse Matrix Multiplication Accelerator Based on Row-Wise Product

Micro, 2020
Sparse-sparse matrix multiplication (SpGEMM) is a computation kernel widely used in numerous application domains such as data analytics, graph processing, and scientific computing.
Nitish Srivastava   +4 more
semanticscholar   +1 more source

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