CN114329329B - 硬件中的稀疏矩阵乘法 - Google Patents

硬件中的稀疏矩阵乘法

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Publication number
CN114329329B
CN114329329B CN202111665133.7A CN202111665133A CN114329329B CN 114329329 B CN114329329 B CN 114329329B CN 202111665133 A CN202111665133 A CN 202111665133A CN 114329329 B CN114329329 B CN 114329329B
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sparse
input
vector
matrix
values
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CN114329329A (zh
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赖纳·阿尔文·波普
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Google LLC
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/76—Architectures of general purpose stored program computers
    • G06F15/80—Architectures of general purpose stored program computers comprising an array of processing units with common control, e.g. single instruction multiple data processors
    • G06F15/8046—Systolic arrays
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G06F7/38—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation
    • G06F7/48—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state device; using unspecified devices
    • G06F7/483—Computations with numbers represented by a non-linear combination of denominational numbers, e.g. rational numbers, logarithmic number system or floating-point numbers
    • G06F7/487—Multiplying; Dividing
    • G06F7/4876—Multiplying
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G06F7/38—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation
    • G06F7/48—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state device; using unspecified devices
    • G06F7/50—Adding; Subtracting
    • G06F7/501—Half or full adders, i.e. basic adder cells for one denomination
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G06F7/76—Arrangements for rearranging, permuting or selecting data according to predetermined rules, independently of the content of the data
    • G06F7/78—Arrangements for rearranging, permuting or selecting data according to predetermined rules, independently of the content of the data for changing the order of data flow, e.g. matrix transposition or LIFO buffers; Overflow or underflow handling therefor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0495—Quantised networks; Sparse networks; Compressed networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Mathematical Optimization (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Algebra (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Nonlinear Science (AREA)
  • Computer Hardware Design (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Complex Calculations (AREA)
  • Neurology (AREA)
  • Advance Control (AREA)
CN202111665133.7A 2021-05-25 2021-12-30 硬件中的稀疏矩阵乘法 Active CN114329329B (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US17/329,259 US12189710B2 (en) 2021-05-25 2021-05-25 Sparse matrix multiplication in hardware
US17/329,259 2021-05-25

Publications (2)

Publication Number Publication Date
CN114329329A CN114329329A (zh) 2022-04-12
CN114329329B true CN114329329B (zh) 2026-02-17

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US (2) US12189710B2 (https=)
EP (1) EP4095719A1 (https=)
JP (2) JP7401513B2 (https=)
KR (2) KR102601034B1 (https=)
CN (1) CN114329329B (https=)

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220012304A1 (en) * 2020-07-07 2022-01-13 Sudarshan Kumar Fast matrix multiplication
US11940907B2 (en) * 2021-06-25 2024-03-26 Intel Corporation Methods and apparatus for sparse tensor storage for neural network accelerators
US20220012012A1 (en) * 2021-09-24 2022-01-13 Martin Langhammer Systems and Methods for Sparsity Operations in a Specialized Processing Block
US20230267169A1 (en) * 2022-02-24 2023-08-24 Xilinx, Inc. Sparse matrix dense vector multliplication circuitry
CN115470450B (zh) * 2022-08-30 2025-09-05 无锡江南计算技术研究所 一种矩阵乘运算装置及其低开销异常定位方法
WO2024108584A1 (zh) * 2022-11-25 2024-05-30 华为技术有限公司 稀疏算子处理方法及装置
KR20240081961A (ko) * 2022-12-01 2024-06-10 삼성전자주식회사 희소 행렬의 압축 저장 포맷을 변환하는 전자 장치 및 그 동작 방법
KR102745798B1 (ko) * 2023-12-26 2024-12-23 리벨리온 주식회사 데이터 연산 방법 및 이를 지원하는 데이터 연산 장치
US20260111173A1 (en) * 2024-10-17 2026-04-23 Edgecortix Inc. On-chip non-zero value unpacking and distribution
CN119602947B (zh) * 2024-11-14 2025-10-03 西安交通大学 一种面向密码算法bike的二进制多项式乘法器及加密方法

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9697176B2 (en) 2014-11-14 2017-07-04 Advanced Micro Devices, Inc. Efficient sparse matrix-vector multiplication on parallel processors
US9760538B2 (en) 2014-12-22 2017-09-12 Palo Alto Research Center Incorporated Computer-implemented system and method for efficient sparse matrix representation and processing
US10528321B2 (en) 2016-12-07 2020-01-07 Microsoft Technology Licensing, Llc Block floating point for neural network implementations
US10489063B2 (en) 2016-12-19 2019-11-26 Intel Corporation Memory-to-memory instructions to accelerate sparse-matrix by dense-vector and sparse-vector by dense-vector multiplication
US11216722B2 (en) 2016-12-31 2022-01-04 Intel Corporation Hardware accelerator template and design framework for implementing recurrent neural networks
US10180928B2 (en) 2016-12-31 2019-01-15 Intel Corporation Heterogeneous hardware accelerator architecture for processing sparse matrix data with skewed non-zero distributions
WO2018134740A2 (en) * 2017-01-22 2018-07-26 Gsi Technology Inc. Sparse matrix multiplication in associative memory device
US10572568B2 (en) * 2018-03-28 2020-02-25 Intel Corporation Accelerator for sparse-dense matrix multiplication
US10726096B2 (en) 2018-10-12 2020-07-28 Hewlett Packard Enterprise Development Lp Sparse matrix vector multiplication with a matrix vector multiplication unit
KR102838677B1 (ko) * 2019-03-15 2025-07-25 인텔 코포레이션 매트릭스 가속기 아키텍처를 위한 희소 최적화
US11188618B2 (en) * 2019-09-05 2021-11-30 Intel Corporation Sparse matrix multiplication acceleration mechanism
US11663746B2 (en) * 2019-11-15 2023-05-30 Intel Corporation Systolic arithmetic on sparse data
US12141229B2 (en) * 2021-05-19 2024-11-12 Nvidia Corporation Techniques for accelerating matrix multiplication computations using hierarchical representations of sparse matrices

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HyGCN: A GCN Accelerator with Hybrid Architecture;YAN MINGYU 等;2020 IEEE INTERNATIONAL SYMPOSIUM ON HIGH PERFORMANCE COMPUTER ARCHITECTURE (HPCA);20200413;第15-29页 *

Also Published As

Publication number Publication date
KR20220159257A (ko) 2022-12-02
US20220382829A1 (en) 2022-12-01
KR102601034B1 (ko) 2023-11-09
US12189710B2 (en) 2025-01-07
JP7793585B2 (ja) 2026-01-05
EP4095719A1 (en) 2022-11-30
US20250068694A1 (en) 2025-02-27
JP2024028901A (ja) 2024-03-05
KR20230155417A (ko) 2023-11-10
CN114329329A (zh) 2022-04-12
JP7401513B2 (ja) 2023-12-19
JP2022181161A (ja) 2022-12-07

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