SG11202000384PA - Ultra-low power neuromorphic artificial intelligence computing accelerator - Google Patents
Ultra-low power neuromorphic artificial intelligence computing acceleratorInfo
- Publication number
- SG11202000384PA SG11202000384PA SG11202000384PA SG11202000384PA SG11202000384PA SG 11202000384P A SG11202000384P A SG 11202000384PA SG 11202000384P A SG11202000384P A SG 11202000384PA SG 11202000384P A SG11202000384P A SG 11202000384PA SG 11202000384P A SG11202000384P A SG 11202000384PA
- Authority
- SG
- Singapore
- Prior art keywords
- ultra
- low power
- artificial intelligence
- intelligence computing
- computing accelerator
- Prior art date
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3234—Power saving characterised by the action undertaken
- G06F1/3243—Power saving in microcontroller unit
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3234—Power saving characterised by the action undertaken
- G06F1/3287—Power saving characterised by the action undertaken by switching off individual functional units in the computer system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3234—Power saving characterised by the action undertaken
- G06F1/3296—Power saving characterised by the action undertaken by lowering the supply or operating voltage
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of 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
- G06N3/065—Analogue means
-
- 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
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G11—INFORMATION STORAGE
- G11C—STATIC STORES
- G11C11/00—Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor
- G11C11/005—Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor comprising combined but independently operative RAM-ROM, RAM-PROM, RAM-EPROM cells
-
- G—PHYSICS
- G11—INFORMATION STORAGE
- G11C—STATIC STORES
- G11C11/00—Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor
- G11C11/02—Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using magnetic elements
- G11C11/16—Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using magnetic elements using elements in which the storage effect is based on magnetic spin effect
-
- G—PHYSICS
- G11—INFORMATION STORAGE
- G11C—STATIC STORES
- G11C11/00—Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor
- G11C11/54—Digital stores characterised by the use of particular electric or magnetic storage elements; Storage elements therefor using elements simulating biological cells, e.g. neuron
-
- G—PHYSICS
- G11—INFORMATION STORAGE
- G11C—STATIC STORES
- G11C13/00—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00, or G11C25/00
- G11C13/0002—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00, or G11C25/00 using resistive RAM [RRAM] elements
- G11C13/0004—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00, or G11C25/00 using resistive RAM [RRAM] elements comprising amorphous/crystalline phase transition cells
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
- H01L25/00—Assemblies consisting of a plurality of individual semiconductor or other solid state devices ; Multistep manufacturing processes thereof
- H01L25/18—Assemblies consisting of a plurality of individual semiconductor or other solid state devices ; Multistep manufacturing processes thereof the devices being of types provided for in two or more different subgroups of the same main group of groups H01L27/00 - H01L33/00, or in a single subclass of H10K, H10N
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
- H01L25/00—Assemblies consisting of a plurality of individual semiconductor or other solid state devices ; Multistep manufacturing processes thereof
- H01L25/50—Multistep manufacturing processes of assemblies consisting of devices, each device being of a type provided for in group H01L27/00 or H01L29/00
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/047—Probabilistic or stochastic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/50—Reducing energy consumption in communication networks in wire-line communication networks, e.g. low power modes or reduced link rate
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Hardware Design (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Neurology (AREA)
- Power Engineering (AREA)
- Condensed Matter Physics & Semiconductors (AREA)
- Chemical & Material Sciences (AREA)
- Crystallography & Structural Chemistry (AREA)
- Manufacturing & Machinery (AREA)
- Image Analysis (AREA)
- Control Of Transmission Device (AREA)
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201762553447P | 2017-09-01 | 2017-09-01 | |
US16/119,929 US11609623B2 (en) | 2017-09-01 | 2018-08-31 | Ultra-low power neuromorphic artificial intelligence computing accelerator |
PCT/US2018/049287 WO2019046835A1 (en) | 2017-09-01 | 2018-09-01 | Ultra-low power neuromorphic artificial intelligence computing accelerator |
Publications (1)
Publication Number | Publication Date |
---|---|
SG11202000384PA true SG11202000384PA (en) | 2020-03-30 |
Family
ID=65518715
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
SG11202000384PA SG11202000384PA (en) | 2017-09-01 | 2018-09-01 | Ultra-low power neuromorphic artificial intelligence computing accelerator |
Country Status (5)
Country | Link |
---|---|
US (2) | US11609623B2 (en) |
EP (1) | EP3676764A1 (en) |
SG (1) | SG11202000384PA (en) |
TW (1) | TWI795434B (en) |
WO (1) | WO2019046835A1 (en) |
Families Citing this family (20)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11609623B2 (en) | 2017-09-01 | 2023-03-21 | Qualcomm Incorporated | Ultra-low power neuromorphic artificial intelligence computing accelerator |
US10768685B2 (en) * | 2017-10-29 | 2020-09-08 | Shanghai Cambricon Information Technology Co., Ltd | Convolutional operation device and method |
US11341397B1 (en) | 2018-04-20 | 2022-05-24 | Perceive Corporation | Computation of neural network node |
US10740434B1 (en) | 2018-04-20 | 2020-08-11 | Perceive Corporation | Reduced dot product computation circuit |
US11783167B1 (en) | 2018-04-20 | 2023-10-10 | Perceive Corporation | Data transfer for non-dot product computations on neural network inference circuit |
US11568227B1 (en) | 2018-04-20 | 2023-01-31 | Perceive Corporation | Neural network inference circuit read controller with multiple operational modes |
US11586910B1 (en) | 2018-04-20 | 2023-02-21 | Perceive Corporation | Write cache for neural network inference circuit |
US11481612B1 (en) | 2018-04-20 | 2022-10-25 | Perceive Corporation | Storage of input values across multiple cores of neural network inference circuit |
KR102382186B1 (en) * | 2018-10-10 | 2022-04-05 | 삼성전자주식회사 | High performance computing system for deep learning |
US11347297B1 (en) * | 2019-01-23 | 2022-05-31 | Perceive Corporation | Neural network inference circuit employing dynamic memory sleep |
US11941533B1 (en) | 2019-05-21 | 2024-03-26 | Perceive Corporation | Compiler for performing zero-channel removal |
EP3994573A4 (en) * | 2019-07-03 | 2022-08-10 | Huaxia General Processor Technologies Inc. | System and architecture of pure functional neural network accelerator |
CN111104459A (en) * | 2019-08-22 | 2020-05-05 | 华为技术有限公司 | Storage device, distributed storage system, and data processing method |
EP4055454A4 (en) * | 2019-11-06 | 2024-01-31 | Nanotronics Imaging Inc | Systems, methods, and media for manufacturing processes |
KR102453628B1 (en) * | 2019-11-26 | 2022-10-12 | 한국전자기술연구원 | Low Power Deep Learning Accelerator |
CN110991634B (en) * | 2019-12-04 | 2022-05-10 | 腾讯科技(深圳)有限公司 | Artificial intelligence accelerator, equipment, chip and data processing method |
US20230197711A1 (en) * | 2020-05-28 | 2023-06-22 | Panasonic Intellectual Property Management Co., Ltd. | Ai chip |
US11693699B2 (en) | 2020-07-02 | 2023-07-04 | Apple Inc. | Hybrid memory in a dynamically power gated hardware accelerator |
TWI725914B (en) * | 2020-08-31 | 2021-04-21 | 國立清華大學 | Neuromorphic system and method for switching between functional operations |
US11449126B1 (en) * | 2021-06-01 | 2022-09-20 | Maxim Integrated Products, Inc. | Power control systems and methods for machine learning computing resources |
Family Cites Families (18)
Publication number | Priority date | Publication date | Assignee | Title |
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JP3699901B2 (en) * | 2001-03-12 | 2005-09-28 | 株式会社東芝 | Integrated circuit power evaluation method |
US6834353B2 (en) * | 2001-10-22 | 2004-12-21 | International Business Machines Corporation | Method and apparatus for reducing power consumption of a processing integrated circuit |
US8510244B2 (en) | 2009-03-20 | 2013-08-13 | ISC8 Inc. | Apparatus comprising artificial neuronal assembly |
US20100262773A1 (en) * | 2009-04-08 | 2010-10-14 | Google Inc. | Data striping in a flash memory data storage device |
US8386690B2 (en) | 2009-11-13 | 2013-02-26 | International Business Machines Corporation | On-chip networks for flexible three-dimensional chip integration |
US8547769B2 (en) * | 2011-03-31 | 2013-10-01 | Intel Corporation | Energy efficient power distribution for 3D integrated circuit stack |
US8977578B1 (en) | 2012-06-27 | 2015-03-10 | Hrl Laboratories, Llc | Synaptic time multiplexing neuromorphic network that forms subsets of connections during different time slots |
US9563841B2 (en) * | 2012-07-31 | 2017-02-07 | International Business Machines Corporation | Globally asynchronous and locally synchronous (GALS) neuromorphic network |
US9336144B2 (en) | 2013-07-25 | 2016-05-10 | Globalfoundries Inc. | Three-dimensional processing system having multiple caches that can be partitioned, conjoined, and managed according to more than one set of rules and/or configurations |
US10409165B2 (en) | 2014-12-15 | 2019-09-10 | Asml Netherlands B.V. | Optimization based on machine learning |
US9886193B2 (en) | 2015-05-15 | 2018-02-06 | International Business Machines Corporation | Architecture and implementation of cortical system, and fabricating an architecture using 3D wafer scale integration |
US10832127B2 (en) | 2015-11-30 | 2020-11-10 | Samsung Electronics Co., Ltd. | Three-dimensional integration of neurosynaptic chips |
CN105892989B (en) | 2016-03-28 | 2017-04-12 | 中国科学院计算技术研究所 | Neural network accelerator and operational method thereof |
US9646243B1 (en) | 2016-09-12 | 2017-05-09 | International Business Machines Corporation | Convolutional neural networks using resistive processing unit array |
CN106485317A (en) | 2016-09-26 | 2017-03-08 | 上海新储集成电路有限公司 | A kind of neutral net accelerator and the implementation method of neural network model |
CN106951961B (en) | 2017-02-24 | 2019-11-26 | 清华大学 | A kind of convolutional neural networks accelerator that coarseness is restructural and system |
US9928460B1 (en) * | 2017-06-16 | 2018-03-27 | Google Llc | Neural network accelerator tile architecture with three-dimensional stacking |
US11609623B2 (en) | 2017-09-01 | 2023-03-21 | Qualcomm Incorporated | Ultra-low power neuromorphic artificial intelligence computing accelerator |
-
2018
- 2018-08-31 US US16/119,929 patent/US11609623B2/en active Active
- 2018-09-01 EP EP18773318.3A patent/EP3676764A1/en active Pending
- 2018-09-01 SG SG11202000384PA patent/SG11202000384PA/en unknown
- 2018-09-01 WO PCT/US2018/049287 patent/WO2019046835A1/en unknown
- 2018-09-03 TW TW107130819A patent/TWI795434B/en active
-
2022
- 2022-06-14 US US17/840,265 patent/US11733766B2/en active Active
Also Published As
Publication number | Publication date |
---|---|
US20190073585A1 (en) | 2019-03-07 |
US11733766B2 (en) | 2023-08-22 |
EP3676764A1 (en) | 2020-07-08 |
TWI795434B (en) | 2023-03-11 |
US20220308653A1 (en) | 2022-09-29 |
US11609623B2 (en) | 2023-03-21 |
TW201921295A (en) | 2019-06-01 |
WO2019046835A1 (en) | 2019-03-07 |
CN110998486A (en) | 2020-04-10 |
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