GB2587021B - Physical implementation of artificial neural networks - Google Patents
Physical implementation of artificial neural networks Download PDFInfo
- Publication number
- GB2587021B GB2587021B GB1913275.2A GB201913275A GB2587021B GB 2587021 B GB2587021 B GB 2587021B GB 201913275 A GB201913275 A GB 201913275A GB 2587021 B GB2587021 B GB 2587021B
- Authority
- GB
- United Kingdom
- Prior art keywords
- neural networks
- artificial neural
- physical implementation
- implementation
- physical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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Classifications
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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
- G06N3/08—Learning methods
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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
- G06N3/06—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 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
-
- 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
- G06N3/0499—Feedforward 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
- G06N3/09—Supervised learning
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Neurology (AREA)
- Logic Circuits (AREA)
- Semiconductor Memories (AREA)
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GB1913275.2A GB2587021B (en) | 2019-09-13 | 2019-09-13 | Physical implementation of artificial neural networks |
| EP20772380.0A EP4028955A1 (en) | 2019-09-13 | 2020-09-09 | Physical implementation of artificial neural networks |
| PCT/GB2020/052166 WO2021048542A1 (en) | 2019-09-13 | 2020-09-09 | Physical implementation of artificial neural networks |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GB1913275.2A GB2587021B (en) | 2019-09-13 | 2019-09-13 | Physical implementation of artificial neural networks |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| GB201913275D0 GB201913275D0 (en) | 2019-10-30 |
| GB2587021A GB2587021A (en) | 2021-03-17 |
| GB2587021B true GB2587021B (en) | 2023-09-13 |
Family
ID=68315241
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| GB1913275.2A Active GB2587021B (en) | 2019-09-13 | 2019-09-13 | Physical implementation of artificial neural networks |
Country Status (3)
| Country | Link |
|---|---|
| EP (1) | EP4028955A1 (en) |
| GB (1) | GB2587021B (en) |
| WO (1) | WO2021048542A1 (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112990444B (en) * | 2021-05-13 | 2021-09-24 | 电子科技大学 | Hybrid neural network training method, system, equipment and storage medium |
-
2019
- 2019-09-13 GB GB1913275.2A patent/GB2587021B/en active Active
-
2020
- 2020-09-09 WO PCT/GB2020/052166 patent/WO2021048542A1/en not_active Ceased
- 2020-09-09 EP EP20772380.0A patent/EP4028955A1/en active Pending
Non-Patent Citations (5)
| Title |
|---|
| AGRAWAL et al., "X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks", 26 June 2019, available at https://arxiv.org/abs/1907.00285 * |
| JOKSAS et al., "Committee Machines - A Universal Method to Deal with Non-Idealities in RRAM-Based Neural Networks", 14 September 2019, available at https://arxiv.org/abs/1909.06658 * |
| LI et al., "2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC", 2015, IEEE, "Merging the Interface: Power, Area and Accuracy Co-optimization for RRAM Crossbar-based Mixed-Signal Computing System" * |
| MEHONIC et al., Frontiers in Neuroscience, 2019, volume 13 page 593, "Simulation of Inference Accuracy Using Realistic RRAM Devices" * |
| PAYVAND et al., "A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: From mitigation to exploitation", 13 July 2018, available at https://arxiv.org/abs/1807.05128 * |
Also Published As
| Publication number | Publication date |
|---|---|
| EP4028955A1 (en) | 2022-07-20 |
| WO2021048542A1 (en) | 2021-03-18 |
| GB2587021A (en) | 2021-03-17 |
| GB201913275D0 (en) | 2019-10-30 |
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