Results 11 to 20 of about 2,725,619 (370)

Matrix Multiplication Reductions

International Workshop and International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
In this paper we study a worst case to average case reduction for the problem of matrix multiplication over finite fields. Suppose we have an efficient average case algorithm, that given two random matrices $A,B$ outputs a matrix that has a non-trivial correlation with their product $A \cdot B$.
Gola, Ashish, Shinkar, Igor, Singh, Harsimran   +2 more
openaire   +5 more sources

Faster quantum subroutine for matrix chain multiplication via Chebyshev approximation [PDF]

Scientific Reports
Matrix operations are crucial to various computational tasks in various fields, and quantum computing offers a promising avenue to accelerate these operations. We present a quantum matrix multiplication (QMM) algorithm that employs amplitude encoding and
Xinying Li, Pei-Lin Zheng, Chengkang Pan, Fei Wang, Chunfeng Cui, Xian Lu   +5 more
doaj   +2 more sources

On the Capacity and Straggler-Robustness of Distributed Secure Matrix Multiplication

IEEE Access, 2019
Computationally efficient matrix multiplication is a fundamental requirement in various fields, including and particularly in data analytics. To do so, the computation task of large-scale matrix multiplication is typically outsourced to multiple servers.
Jaber Kakar, Seyedhamed Ebadifar, Aydin Sezgin   +2 more
doaj   +2 more sources

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale [PDF]

arXiv.org, 2022
Large language models have been widely adopted but require significant GPU memory for inference. We develop a procedure for Int8 matrix multiplication for feed-forward and attention projection layers in transformers, which cut the memory needed for ...
Tim Dettmers, M. Lewis, Younes Belkada, Luke Zettlemoyer   +3 more
semanticscholar   +1 more source

New Bounds for Matrix Multiplication: from Alpha to Omega [PDF]

ACM-SIAM Symposium on Discrete Algorithms, 2023
The main contribution of this paper is a new improved variant of the laser method for designing matrix multiplication algorithms. Building upon the recent techniques of [Duan, Wu, Zhou FOCS'2023], the new method introduces several new ingredients that ...
V. V. Williams, Yinzhan Xu, Zixuan Xu, Renfei Zhou   +3 more
semanticscholar   +1 more source

Inverse-designed low-index-contrast structures on a silicon photonics platform for vector–matrix multiplication [PDF]

Nature Photonics, 2023
Inverse-designed silicon photonic metastructures offer an efficient platform to perform analogue computations with electromagnetic waves. However, due to computational difficulties, scaling up these metastructures to handle a large number of data ...
Vahid Nikkhah, Ali Pirmoradi, F. Ashtiani, Brian Edwards, Firooz Aflatouni, N. Engheta   +5 more
semanticscholar   +1 more source

Faster Matrix Multiplication via Asymmetric Hashing [PDF]

IEEE Annual Symposium on Foundations of Computer Science, 2022
Fast matrix multiplication is one of the most fundamental problems in algorithm research. The exponent of the optimal time complexity of matrix multiplication is usually denoted by $\omega$.
Ran Duan, Hongxun Wu, Renfei Zhou
semanticscholar   +1 more source

Faster Rectangular Matrix Multiplication by Combination Loss Analysis [PDF]

ACM-SIAM Symposium on Discrete Algorithms, 2023
Duan, Wu and Zhou (FOCS 2023) recently obtained the improved upper bound on the exponent of square matrix multiplication ...
Franccois Le Gall
semanticscholar   +1 more source

Sextans: A Streaming Accelerator for General-Purpose Sparse-Matrix Dense-Matrix Multiplication [PDF]

Symposium on Field Programmable Gate Arrays, 2021
Sparse-Matrix Dense-Matrix multiplication (SpMM) is the key operator for a wide range of applications including scientific computing, graph processing, and deep learning.
Linghao Song, Yuze Chi, Atefeh Sohrabizadeh, Young-kyu Choi, Jason Lau, J. Cong   +5 more
semanticscholar   +1 more source

Neon NTT: Faster Dilithium, Kyber, and Saber on Cortex-A72 and Apple M1

Transactions on Cryptographic Hardware and Embedded Systems, 2021
We present new speed records on the Armv8-A architecture for the latticebased schemes Dilithium, Kyber, and Saber. The core novelty in this paper is the combination of Montgomery multiplication and Barrett reduction resulting in “Barrett multiplication ...
Hanno Becker, Vincent Hwang, Matthias J. Kannwischer, Bo-Yin Yang, Shang-Yi Yang   +4 more
doaj   +1 more source