Lightweight Modal Regression for Stand Alone Embedded Systems
| Autor: | Koichiro Yamauchi, Taiki Watanabe |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.product_category
Computer science Multivalued function business.industry Kernel density estimation Regression analysis 02 engineering and technology Construct (python library) 01 natural sciences 010104 statistics & probability Modal Embedded system 0202 electrical engineering electronic engineering information engineering Internet access 020201 artificial intelligence & image processing 0101 mathematics business |
| Zdroj: | Neural Information Processing ISBN: 9783030367107 ICONIP (2) |
| DOI: | 10.1007/978-3-030-36711-4_31 |
| Popis: | Although the CPU power of recent embedded systems has increased, their storage space is still limited. To overcome this limitation, most embedded devices are connected to a cloud server so they can outsource heavy calculations. However, some applications must handle private data, meaning internet connections are undesirable based on security concerns. Therefore, small devices that handle private data should be able to work without internet connections. This paper presents a limited modal regression model that restricts the number of internal units to a certain fixed number. Modal regression can be used for multivalued function approximation with limited sensory inputs. In this study, a kernel density estimator (KDE) with a fixed number of kernels called “limited KDE” was constructed. We will demonstrate how to implement the limited KDE and how to construct a lightweight algorithm for modal regression using a system-on-chip field-programmable gate array device. |
| Databáze: | OpenAIRE |
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