An Integrated Circuit for Two-Dimensional Edge-Detection with Local Adaptation Based on Retinal Networks

Yamada, Hitoshi; Miyashita, Takashige; Ohtani, Masahiro; Nishio, Kimihiro; Yonezu, Hiroo; Furukawa, Yukio

doi:10.1007/s10043-002-0001-8

Cited by 22 publications

(4 citation statements)

References 9 publications

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“…In contrast, for analog memory, a refresh process is difficult because linear analog data is stored, and it needs a time analysis system based on an electric charge reduction curve. Studies have proposed memory methods for connecting weights in neural networks, such as floating gate type devices [5] and magnetic storage [6].…”

Section: Analog Hardware Neural Networkmentioning

confidence: 99%

Analog Neural Network Model based on Logarithmic Four-Quadrant Multipliers

Kawaguchi

Ikuta

Ishii

et al. 2023

International Journal of Smart Computing and Artificial Intelligence

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Models for artificial intelligence, machine learning, and neural networks are implemented on digital computers with a von Neumann architecture. Few studies have considered analog neural networks. In our previous study, we used multipliers for representing connecting weights in a neural network. The multipliers calculate the product of input signals and their corresponding connecting weights. However, their operating range is limited by semiconductor characteristics. The input and output ranges for networks that use these multipliers are thus limited. Furthermore, the circuit operation becomes unstable. Here, we propose a logarithmic four-quadrant multiplier for representing connecting weights. Experiments show that this multiplier exhibits stable operation over a wide range. A model that uses only analog electronic circuits is presented. Its learning time is quite short compared to that for models implemented on a digital computer. We increased the number of units and network layers. We suggest the possibility of a hardware implementation of a deep learning model.

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Section: Analog Hardware Neural Networkmentioning

confidence: 99%

Analog Neural Network Model based on Logarithmic Four-Quadrant Multipliers

Kawaguchi

Ikuta

Ishii

et al. 2023

International Journal of Smart Computing and Artificial Intelligence

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“…In this case, D/A and A/D conversion causes an overhead problem. Other memorizing methods are the floatage gate type device, ferroelectric memory (FeRAM) and magnetic substance memories (MRAM) (Luthon & Dragomirescu, 1999;Yamada et al, 2000).…”

Section: Analog Neural Networkmentioning

confidence: 99%

Analog Learning Neural Network using Two-Stage Mode by Multiple and Sample Hold Circuits

Kawaguchi

Ishii

Jimbo

2014

International Journal of Software Innovation

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In the neural network field, many application models have been proposed. A neuro chip and an artificial retina chip are developed to comprise the neural network model and simulate the biomedical vision system. Previous analog neural network models were composed of the operational amplifier and fixed resistance. It is difficult to change the connection coefficient. In this study, we used analog electronic multiple and sample hold circuits. The connecting weights describe the input voltage. It is easy to change the connection coefficient. This model works only on analog electronic circuits. It can finish the learning process in a very short time and this model will enable more flexible learning.

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“…A number of smart vision chips have been introduced [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. The principle of edge detection with the smart vision chip is shown in Fig.…”

Section: Principle Of Edge Detectionmentioning

confidence: 99%

“…Since the 1990s, it has become possible to integrate image signal processing circuits into a single chip due to progress in the field of complementary metal oxide semiconductor(CMOS) technology [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The smart vision chip inspired by the human retina also exhibits a lower power dissipation and a higher operating speed [1].…”

Section: Introductionmentioning

confidence: 99%

Light-Adaptive Vision System for Remote Surveillance Using an Edge Detection Vision Chip

Choi¹,

Jo²,

Seo³

et al. 2011

Journal of Sensor Science and Technology

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In this paper, we propose a vision system using a field programmable gate array(FPGA) and a smart vision chip. The output of the vision chip is varied by illumination conditions. This chip is suitable as a surveillance system in a dynamic environment. However, because the output swing of a smart vision chip is too small to definitely confirm the warning signal with the FPGA, a modification was needed for a reliable signal. The proposed system is based on a transmission control protocol/internet protocol(TCP/IP) that enables monitoring from a remote place. The warning signal indicates that some objects are too near.

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An Integrated Circuit for Two-Dimensional Edge-Detection with Local Adaptation Based on Retinal Networks

Cited by 22 publications

References 9 publications

Analog Neural Network Model based on Logarithmic Four-Quadrant Multipliers

Analog Neural Network Model based on Logarithmic Four-Quadrant Multipliers

Analog Learning Neural Network using Two-Stage Mode by Multiple and Sample Hold Circuits

Light-Adaptive Vision System for Remote Surveillance Using an Edge Detection Vision Chip

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