JP2022506345A - ドット積計算機およびその演算方法 - Google Patents
ドット積計算機およびその演算方法 Download PDFInfo
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- JP2022506345A JP2022506345A JP2021523664A JP2021523664A JP2022506345A JP 2022506345 A JP2022506345 A JP 2022506345A JP 2021523664 A JP2021523664 A JP 2021523664A JP 2021523664 A JP2021523664 A JP 2021523664A JP 2022506345 A JP2022506345 A JP 2022506345A
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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
- 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
- G06F7/52—Multiplying; Dividing
- G06F7/523—Multiplying only
-
- 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
- G06F7/544—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 for evaluating functions by calculation
- G06F7/5443—Sum of products
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/30—Arrangements for executing machine instructions, e.g. instruction decode
- G06F9/30003—Arrangements for executing specific machine instructions
- G06F9/30007—Arrangements for executing specific machine instructions to perform operations on data operands
- G06F9/3001—Arithmetic instructions
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/30—Arrangements for executing machine instructions, e.g. instruction decode
- G06F9/30003—Arrangements for executing specific machine instructions
- G06F9/30007—Arrangements for executing specific machine instructions to perform operations on data operands
- G06F9/30018—Bit or string instructions
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/30—Arrangements for executing machine instructions, e.g. instruction decode
- G06F9/30003—Arrangements for executing specific machine instructions
- G06F9/30007—Arrangements for executing specific machine instructions to perform operations on data operands
- G06F9/30036—Instructions to perform operations on packed data, e.g. vector, tile or matrix operations
- G06F9/30038—Instructions to perform operations on packed data, e.g. vector, tile or matrix operations using a mask
-
- 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
- G06N3/0464—Convolutional networks [CNN, ConvNet]
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- 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
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2207/00—Indexing scheme relating to methods or arrangements for processing data by operating upon the order or content of the data handled
- G06F2207/38—Indexing scheme relating to groups G06F7/38 - G06F7/575
- G06F2207/48—Indexing scheme relating to groups G06F7/48 - G06F7/575
- G06F2207/4802—Special implementations
- G06F2207/4818—Threshold devices
- G06F2207/4824—Neural 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Data Mining & Analysis (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Artificial Intelligence (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Neurology (AREA)
- Complex Calculations (AREA)
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US16/184,985 | 2018-11-08 | ||
| US16/184,985 US10768895B2 (en) | 2018-11-08 | 2018-11-08 | Dot product calculators and methods of operating the same |
| PCT/EP2019/080136 WO2020094586A1 (en) | 2018-11-08 | 2019-11-04 | Dot product calculators and methods of operating the same |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JP2022506345A true JP2022506345A (ja) | 2022-01-17 |
| JP2022506345A5 JP2022506345A5 (https=) | 2022-10-20 |
Family
ID=68461801
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2021523664A Ceased JP2022506345A (ja) | 2018-11-08 | 2019-11-04 | ドット積計算機およびその演算方法 |
Country Status (7)
| Country | Link |
|---|---|
| US (3) | US10768895B2 (https=) |
| EP (1) | EP3877839A1 (https=) |
| JP (1) | JP2022506345A (https=) |
| KR (1) | KR20210092751A (https=) |
| CN (1) | CN113330421B (https=) |
| DE (1) | DE112019005586T5 (https=) |
| WO (1) | WO2020094586A1 (https=) |
Families Citing this family (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11169809B2 (en) * | 2017-03-31 | 2021-11-09 | Intel Corporation | Method and apparatus for converting scatter control elements to gather control elements used to sort vector data elements |
| US10409614B2 (en) | 2017-04-24 | 2019-09-10 | Intel Corporation | Instructions having support for floating point and integer data types in the same register |
| US10474458B2 (en) | 2017-04-28 | 2019-11-12 | Intel Corporation | Instructions and logic to perform floating-point and integer operations for machine learning |
| CN110770722B (zh) * | 2017-06-29 | 2023-08-18 | 北京清影机器视觉技术有限公司 | 二维数据匹配方法、装置和逻辑电路 |
| US10768895B2 (en) | 2018-11-08 | 2020-09-08 | Movidius Limited | Dot product calculators and methods of operating the same |
| US12182035B2 (en) | 2019-03-15 | 2024-12-31 | Intel Corporation | Systems and methods for cache optimization |
| US11934342B2 (en) | 2019-03-15 | 2024-03-19 | Intel Corporation | Assistance for hardware prefetch in cache access |
| WO2020190807A1 (en) * | 2019-03-15 | 2020-09-24 | Intel Corporation | Systolic disaggregation within a matrix accelerator architecture |
| PL3938914T3 (pl) | 2019-03-15 | 2025-03-31 | Intel Corporation | Dynamiczna rekonfiguracja pamięci |
| US12131251B2 (en) * | 2019-03-19 | 2024-10-29 | Cirrus Logic Inc. | Neurons for artificial neural networks |
| WO2020218157A1 (ja) * | 2019-04-25 | 2020-10-29 | 国立大学法人静岡大学 | 予測システム、予測方法、および予測プログラム |
| US11741349B2 (en) * | 2019-10-31 | 2023-08-29 | Arm Limited | Performing matrix-vector multiply operations for neural networks on electronic devices |
| US11861761B2 (en) | 2019-11-15 | 2024-01-02 | Intel Corporation | Graphics processing unit processing and caching improvements |
| US11663746B2 (en) | 2019-11-15 | 2023-05-30 | Intel Corporation | Systolic arithmetic on sparse data |
| US11500680B2 (en) * | 2020-04-24 | 2022-11-15 | Alibaba Group Holding Limited | Systolic array-friendly data placement and control based on masked write |
| US20220405571A1 (en) * | 2021-06-16 | 2022-12-22 | Microsoft Technology Licensing, Llc | Sparsifying narrow data formats for neural networks |
| US12321857B2 (en) | 2021-06-24 | 2025-06-03 | Intel Corporation | Methods and apparatus to perform machine-learning model operations on sparse accelerators |
| US20230035474A1 (en) * | 2021-07-20 | 2023-02-02 | Gsi Technology Inc. | Compiler for a parallel processor |
| US20230083270A1 (en) * | 2021-09-14 | 2023-03-16 | International Business Machines Corporation | Mixed signal circuitry for bitwise multiplication with different accuracies |
| US11789646B2 (en) | 2021-09-24 | 2023-10-17 | Intel Corporation | Methods, apparatus, and articles of manufacture to increase data reuse for multiply and accumulate (MAC) operations |
| US11669489B2 (en) * | 2021-09-30 | 2023-06-06 | International Business Machines Corporation | Sparse systolic array design |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS62233884A (ja) * | 1986-04-04 | 1987-10-14 | Hitachi Ltd | ベクトル・プロセツサ |
| JP2011511986A (ja) * | 2008-02-11 | 2011-04-14 | リニア アルジェブラ テクノロジーズ リミテッド | プロセッサ |
| WO2018080624A1 (en) * | 2016-10-27 | 2018-05-03 | Google Llc | Exploiting input data sparsity in neural network compute units |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080071851A1 (en) * | 2006-09-20 | 2008-03-20 | Ronen Zohar | Instruction and logic for performing a dot-product operation |
| US20160358069A1 (en) * | 2015-06-03 | 2016-12-08 | Samsung Electronics Co., Ltd. | Neural network suppression |
| US10223359B2 (en) * | 2016-10-10 | 2019-03-05 | The Directv Group, Inc. | Determining recommended media programming from sparse consumption data |
| US10146738B2 (en) * | 2016-12-31 | 2018-12-04 | Intel Corporation | Hardware accelerator architecture for processing very-sparse and hyper-sparse matrix data |
| US20180189675A1 (en) * | 2016-12-31 | 2018-07-05 | Intel Corporation | Hardware accelerator architecture and template for web-scale k-means clustering |
| US10096134B2 (en) * | 2017-02-01 | 2018-10-09 | Nvidia Corporation | Data compaction and memory bandwidth reduction for sparse neural networks |
| US10171084B2 (en) * | 2017-04-24 | 2019-01-01 | The Regents Of The University Of Michigan | Sparse coding with Memristor networks |
| US20180330235A1 (en) * | 2017-05-15 | 2018-11-15 | National Taiwan University | Apparatus and Method of Using Dual Indexing in Input Neurons and Corresponding Weights of Sparse Neural Network |
| TWI684141B (zh) | 2017-10-12 | 2020-02-01 | 英屬開曼群島商意騰科技股份有限公司 | 人工神經元中以非零封包加速乘法運算的裝置及方法 |
| KR102697300B1 (ko) * | 2018-03-07 | 2024-08-23 | 삼성전자주식회사 | 전자 장치 및 머신 러닝 수행 방법 |
| US10768895B2 (en) | 2018-11-08 | 2020-09-08 | Movidius Limited | Dot product calculators and methods of operating the same |
-
2018
- 2018-11-08 US US16/184,985 patent/US10768895B2/en not_active Expired - Fee Related
-
2019
- 2019-11-04 KR KR1020217015961A patent/KR20210092751A/ko not_active Withdrawn
- 2019-11-04 JP JP2021523664A patent/JP2022506345A/ja not_active Ceased
- 2019-11-04 CN CN201980088183.8A patent/CN113330421B/zh active Active
- 2019-11-04 DE DE112019005586.0T patent/DE112019005586T5/de not_active Withdrawn
- 2019-11-04 WO PCT/EP2019/080136 patent/WO2020094586A1/en not_active Ceased
- 2019-11-04 EP EP19798070.9A patent/EP3877839A1/en not_active Withdrawn
-
2020
- 2020-08-24 US US17/001,455 patent/US11023206B2/en not_active Expired - Fee Related
-
2021
- 2021-04-28 US US17/243,282 patent/US11656845B2/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS62233884A (ja) * | 1986-04-04 | 1987-10-14 | Hitachi Ltd | ベクトル・プロセツサ |
| JP2011511986A (ja) * | 2008-02-11 | 2011-04-14 | リニア アルジェブラ テクノロジーズ リミテッド | プロセッサ |
| WO2018080624A1 (en) * | 2016-10-27 | 2018-05-03 | Google Llc | Exploiting input data sparsity in neural network compute units |
Also Published As
| Publication number | Publication date |
|---|---|
| US20200387350A1 (en) | 2020-12-10 |
| US11023206B2 (en) | 2021-06-01 |
| US20210247961A1 (en) | 2021-08-12 |
| US11656845B2 (en) | 2023-05-23 |
| CN113330421A (zh) | 2021-08-31 |
| EP3877839A1 (en) | 2021-09-15 |
| US10768895B2 (en) | 2020-09-08 |
| CN113330421B (zh) | 2024-01-16 |
| WO2020094586A1 (en) | 2020-05-14 |
| US20200150926A1 (en) | 2020-05-14 |
| KR20210092751A (ko) | 2021-07-26 |
| DE112019005586T5 (de) | 2021-12-16 |
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