Presentation 2020-02-26
On Machine Learning Based Accuracy Improvement for A Digital Temperature and Voltage Sensor
Masayuki Gondo, Yousuke Miyake, Seiji Kajihara,
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Abstract(in English) To measure an on-chip temperature and voltage during VLSI operation, an RO(Ring Oscillator)-based digital temperature and voltage sensor has been proposed. Measurement accuracy of the RO-based sensor is decreased due to process variation. While calibration for the sensors is known as a method to relax the influence of process variation, it is not sufficient necessarily for reduction of the influence of process variation that occur in state-of-the-art VLSIs. This work proposes a method for accuracy improvement of the RO-based temperature and voltage sensors. The proposed method employs SVR(Support Vector Regression) which is one of the machine learning techniques and is efficient for reduction of the influence of process variation. In evaluation experiments for test chips with process variation show that the proposed method derives higher measurement accuracy than the existing method using multiple regression analysis.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Temperature sensor / Voltage sensor / Ring Oscillator / Machine learning / Support Vector Regression
Paper # DC2019-86
Date of Issue 2020-02-19 (DC)

Conference Information
Committee DC
Conference Date 2020/2/26(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Satoshi Fukumoto(Tokyo Metropolitan Univ.)
Vice Chair Hiroshi Takahashi(Ehime Univ.)
Secretary Hiroshi Takahashi(Nihon Univ.)
Assistant

Paper Information
Registration To Technical Committee on Dependable Computing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) On Machine Learning Based Accuracy Improvement for A Digital Temperature and Voltage Sensor
Sub Title (in English)
Keyword(1) Temperature sensor
Keyword(2) Voltage sensor
Keyword(3) Ring Oscillator
Keyword(4) Machine learning
Keyword(5) Support Vector Regression
1st Author's Name Masayuki Gondo
1st Author's Affiliation Kyushu Institute of Technology(Kyutech)
2nd Author's Name Yousuke Miyake
2nd Author's Affiliation Kyushu Institute of Technology(Kyutech)
3rd Author's Name Seiji Kajihara
3rd Author's Affiliation Kyushu Institute of Technology(Kyutech)
Date 2020-02-26
Paper # DC2019-86
Volume (vol) vol.119
Number (no) DC-420
Page pp.pp.1-6(DC),
#Pages 6
Date of Issue 2020-02-19 (DC)