Generate while Sensing - Intelligent Imaging with Memristive Pixel-CNN
Gated Pixel Convolution Neural Network (Pix-eICNN) is a computationally intensive network that is useful for generating visual data. The prediction and generating pixels is a challenging but useful task for many fields such as forensics, machine vision and robotics. However, implementing PixeICNN in...
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Published in | 2021 IEEE 21st International Conference on Nanotechnology (NANO) pp. 112 - 115 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
28.07.2021
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Subjects | |
Online Access | Get full text |
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Summary: | Gated Pixel Convolution Neural Network (Pix-eICNN) is a computationally intensive network that is useful for generating visual data. The prediction and generating pixels is a challenging but useful task for many fields such as forensics, machine vision and robotics. However, implementing PixeICNN in edge devices is a challenging task due to learning complexity and computational limits. In this paper, we present the design of neuro-memristive circuits for computing PixelCNN cells in analog domain as a coprocessor unit in edge devices. The architecture was designed using 180nm CMOS technology and carbon-chalcogenide memristive devices. On-chip area of the proposed architecture unit is 24.756mm 2 , while power depends on the size of the input image and the configuration of the overall network. The power required to generate the images sequentially is 154.336mW. |
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ISSN: | 1944-9380 |
DOI: | 10.1109/NANO51122.2021.9514312 |