PL3974959T3 - Przyspieszone sprzętowo uczenie maszynowe - Google Patents
Przyspieszone sprzętowo uczenie maszynoweInfo
- Publication number
- PL3974959T3 PL3974959T3 PL21208402.4T PL21208402T PL3974959T3 PL 3974959 T3 PL3974959 T3 PL 3974959T3 PL 21208402 T PL21208402 T PL 21208402T PL 3974959 T3 PL3974959 T3 PL 3974959T3
- Authority
- PL
- Poland
- Prior art keywords
- machine learning
- hardware accelerated
- accelerated machine
- hardware
- learning
- Prior art date
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F5/00—Methods or arrangements for data conversion without changing the order or content of the data handled
- G06F5/01—Methods or arrangements for data conversion without changing the order or content of the data handled for shifting, e.g. justifying, scaling, normalising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06F7/02—Comparing digital values
- G06F7/023—Comparing digital values adaptive, e.g. self learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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; CALCULATING OR 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2205/00—Indexing scheme relating to group G06F5/00; Methods or arrangements for data conversion without changing the order or content of the data handled
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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; CALCULATING OR 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Medical Informatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Health & Medical Sciences (AREA)
- Nonlinear Science (AREA)
- Complex Calculations (AREA)
- Error Detection And Correction (AREA)
- Advance Control (AREA)
- Executing Machine-Instructions (AREA)
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201662276169P | 2016-01-07 | 2016-01-07 | |
US15/399,714 US11170294B2 (en) | 2016-01-07 | 2017-01-05 | Hardware accelerated machine learning |
Publications (1)
Publication Number | Publication Date |
---|---|
PL3974959T3 true PL3974959T3 (pl) | 2023-08-07 |
Family
ID=59274447
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PL21208402.4T PL3974959T3 (pl) | 2016-01-07 | 2017-01-06 | Przyspieszone sprzętowo uczenie maszynowe |
PL21158563.3T PL3893123T3 (pl) | 2016-01-07 | 2017-01-06 | Przyspieszone sprzętowo uczenie maszynowe |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PL21158563.3T PL3893123T3 (pl) | 2016-01-07 | 2017-01-06 | Przyspieszone sprzętowo uczenie maszynowe |
Country Status (5)
Country | Link |
---|---|
US (3) | US11170294B2 (pl) |
EP (4) | EP3893123B1 (pl) |
ES (2) | ES2954562T3 (pl) |
PL (2) | PL3974959T3 (pl) |
WO (1) | WO2017120517A1 (pl) |
Families Citing this family (51)
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US11164071B2 (en) * | 2017-04-18 | 2021-11-02 | Samsung Electronics Co., Ltd. | Method and apparatus for reducing computational complexity of convolutional neural networks |
US10684955B2 (en) | 2017-04-21 | 2020-06-16 | Micron Technology, Inc. | Memory devices and methods which may facilitate tensor memory access with memory maps based on memory operations |
US10817293B2 (en) * | 2017-04-28 | 2020-10-27 | Tenstorrent Inc. | Processing core with metadata actuated conditional graph execution |
US11501139B2 (en) * | 2017-05-03 | 2022-11-15 | Intel Corporation | Scaling half-precision floating point tensors for training deep neural networks |
US10643297B2 (en) * | 2017-05-05 | 2020-05-05 | Intel Corporation | Dynamic precision management for integer deep learning primitives |
DE102018110687A1 (de) | 2017-05-05 | 2018-11-08 | Intel Corporation | Dynamisches Genauigkeitsmanagement für Deep-Learning-Ganzzahlprimitive |
US10809978B2 (en) * | 2017-06-02 | 2020-10-20 | Texas Instruments Incorporated | Merge sort accelerator |
KR20200047551A (ko) | 2017-07-30 | 2020-05-07 | 뉴로블레이드, 리미티드. | 메모리 기반 분산 프로세서 아키텍처 |
US10108538B1 (en) * | 2017-07-31 | 2018-10-23 | Google Llc | Accessing prologue and epilogue data |
GB2567038B (en) * | 2017-07-31 | 2019-09-25 | Google Llc | Accessing prologue and epilogue data |
GB2568776B (en) * | 2017-08-11 | 2020-10-28 | Google Llc | Neural network accelerator with parameters resident on chip |
US11847246B1 (en) * | 2017-09-14 | 2023-12-19 | United Services Automobile Association (Usaa) | Token based communications for machine learning systems |
US10679129B2 (en) * | 2017-09-28 | 2020-06-09 | D5Ai Llc | Stochastic categorical autoencoder network |
US10902318B2 (en) | 2017-11-06 | 2021-01-26 | Neuralmagic Inc. | Methods and systems for improved transforms in convolutional neural networks |
KR20190051697A (ko) | 2017-11-07 | 2019-05-15 | 삼성전자주식회사 | 뉴럴 네트워크의 디컨벌루션 연산을 수행하는 장치 및 방법 |
US20190156214A1 (en) | 2017-11-18 | 2019-05-23 | Neuralmagic Inc. | Systems and methods for exchange of data in distributed training of machine learning algorithms |
WO2019147708A1 (en) * | 2018-01-24 | 2019-08-01 | Alibaba Group Holding Limited | A deep learning accelerator system and methods thereof |
US10620954B2 (en) * | 2018-03-29 | 2020-04-14 | Arm Limited | Dynamic acceleration of data processor operations using data-flow analysis |
US11144316B1 (en) | 2018-04-17 | 2021-10-12 | Ali Tasdighi Far | Current-mode mixed-signal SRAM based compute-in-memory for low power machine learning |
US10592208B2 (en) | 2018-05-07 | 2020-03-17 | International Business Machines Corporation | Very low precision floating point representation for deep learning acceleration |
US11216732B2 (en) | 2018-05-31 | 2022-01-04 | Neuralmagic Inc. | Systems and methods for generation of sparse code for convolutional neural networks |
US10832133B2 (en) | 2018-05-31 | 2020-11-10 | Neuralmagic Inc. | System and method of executing neural networks |
US11449363B2 (en) | 2018-05-31 | 2022-09-20 | Neuralmagic Inc. | Systems and methods for improved neural network execution |
US10963787B2 (en) | 2018-05-31 | 2021-03-30 | Neuralmagic Inc. | Systems and methods for generation of sparse code for convolutional neural networks |
WO2021061172A1 (en) * | 2019-09-27 | 2021-04-01 | Neuralmagic Inc. | System and method of executing neural networks |
KR102607864B1 (ko) * | 2018-07-06 | 2023-11-29 | 삼성전자주식회사 | 뉴로모픽 시스템 및 그것의 동작 방법 |
US10956315B2 (en) * | 2018-07-24 | 2021-03-23 | Micron Technology, Inc. | Memory devices and methods which may facilitate tensor memory access |
CN109543830B (zh) * | 2018-09-20 | 2023-02-03 | 中国科学院计算技术研究所 | 一种用于卷积神经网络加速器的拆分累加器 |
WO2020072274A1 (en) | 2018-10-01 | 2020-04-09 | Neuralmagic Inc. | Systems and methods for neural network pruning with accuracy preservation |
US11379712B2 (en) | 2018-10-09 | 2022-07-05 | Hewlett Packard Enterprise Development Lp | Avoiding cycles in neural networks |
US11544559B2 (en) | 2019-01-08 | 2023-01-03 | Neuralmagic Inc. | System and method for executing convolution in a neural network |
US11748599B2 (en) * | 2019-02-21 | 2023-09-05 | Texas Instruments Incorporated | Super-tiling in neural network processing to enable analytics at lower memory speed |
US10884707B1 (en) | 2019-06-27 | 2021-01-05 | Amazon Technologies, Inc. | Transpose operations using processing element array |
US11195095B2 (en) | 2019-08-08 | 2021-12-07 | Neuralmagic Inc. | System and method of accelerating execution of a neural network |
CN111104459A (zh) * | 2019-08-22 | 2020-05-05 | 华为技术有限公司 | 存储设备、分布式存储系统以及数据处理方法 |
US20210064987A1 (en) * | 2019-09-03 | 2021-03-04 | Nvidia Corporation | Processor and system to convert tensor operations in machine learning |
US10915298B1 (en) | 2019-10-08 | 2021-02-09 | Ali Tasdighi Far | Current mode multiply-accumulate for compute in memory binarized neural networks |
US11610104B1 (en) | 2019-12-30 | 2023-03-21 | Ali Tasdighi Far | Asynchronous analog accelerator for fully connected artificial neural networks |
US11615256B1 (en) | 2019-12-30 | 2023-03-28 | Ali Tasdighi Far | Hybrid accumulation method in multiply-accumulate for machine learning |
US11593628B2 (en) | 2020-03-05 | 2023-02-28 | Apple Inc. | Dynamic variable bit width neural processor |
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-
2017
- 2017-01-05 US US15/399,714 patent/US11170294B2/en active Active
- 2017-01-06 EP EP21158563.3A patent/EP3893123B1/en active Active
- 2017-01-06 WO PCT/US2017/012600 patent/WO2017120517A1/en active Application Filing
- 2017-01-06 ES ES21208402T patent/ES2954562T3/es active Active
- 2017-01-06 ES ES21158563T patent/ES2934979T3/es active Active
- 2017-01-06 PL PL21208402.4T patent/PL3974959T3/pl unknown
- 2017-01-06 PL PL21158563.3T patent/PL3893123T3/pl unknown
- 2017-01-06 EP EP21208402.4A patent/EP3974959B1/en active Active
- 2017-01-06 EP EP23163158.1A patent/EP4220380B1/en active Active
- 2017-01-06 EP EP17736464.3A patent/EP3387549B1/en active Active
-
2021
- 2021-10-14 US US17/501,314 patent/US11816572B2/en active Active
-
2023
- 2023-10-16 US US18/380,620 patent/US20240046088A1/en active Pending
Also Published As
Publication number | Publication date |
---|---|
US20170200094A1 (en) | 2017-07-13 |
ES2934979T3 (es) | 2023-02-28 |
EP4220380B1 (en) | 2024-08-14 |
US20220067522A1 (en) | 2022-03-03 |
EP3387549A4 (en) | 2019-07-24 |
EP4220380A1 (en) | 2023-08-02 |
EP3387549B1 (en) | 2021-02-24 |
ES2954562T3 (es) | 2023-11-23 |
EP3893123B1 (en) | 2022-11-16 |
US11816572B2 (en) | 2023-11-14 |
US11170294B2 (en) | 2021-11-09 |
WO2017120517A1 (en) | 2017-07-13 |
EP3974959B1 (en) | 2023-06-07 |
EP3974959A1 (en) | 2022-03-30 |
EP4220380C0 (en) | 2024-08-14 |
EP3893123A1 (en) | 2021-10-13 |
EP3387549A1 (en) | 2018-10-17 |
US20240046088A1 (en) | 2024-02-08 |
PL3893123T3 (pl) | 2023-03-20 |
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