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Open AccessArticle

Design of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network

1
Department of Semiconductor Science, Dongguk University-Seoul, Seoul 04620, Korea
2
Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Korea
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(11), 3101; https://doi.org/10.3390/s20113101
Received: 17 April 2020 / Revised: 26 May 2020 / Accepted: 29 May 2020 / Published: 30 May 2020
(This article belongs to the Special Issue Smart Image Sensors)
This paper presents an always-on Complementary Metal Oxide Semiconductor (CMOS) image sensor (CIS) using an analog convolutional neural network for image classification in mobile applications. To reduce the power consumption as well as the overall processing time, we propose analog convolution circuits for computing convolution, max-pooling, and correlated double sampling operations without operational transconductance amplifiers. In addition, we used the voltage-mode MAX circuit for max pooling in the analog domain. After the analog convolution processing, the image data were reduced by 99.58% and were converted to digital with a 4-bit single-slope analog-to-digital converter. After the conversion, images were classified by the fully connected processor, which is traditionally performed in the digital domain. The measurement results show that we achieved an 89.33% image classification accuracy. The prototype CIS was fabricated in a 0.11 μm 1-poly 4-metal CIS process with a standard 4T-active pixel sensor. The image resolution was 160 × 120, and the total power consumption of the proposed CIS was 1.12 mW with a 3.3 V supply voltage and a maximum frame rate of 120. View Full-Text
Keywords: always-on; Complementary Metal Oxide Semiconductor (CMOS) image sensor; convolutional neural networks; image classification always-on; Complementary Metal Oxide Semiconductor (CMOS) image sensor; convolutional neural networks; image classification
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Choi, J.; Lee, S.; Son, Y.; Kim, S.Y. Design of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network. Sensors 2020, 20, 3101.

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