Generate while Sensing - Intelligent Imaging with Memristive Pixel-CNN

• Published in: 2021 IEEE 21st International Conference on Nanotechnology (NANO) pp. 112 - 115
• Main Authors: Bakambekova, A., Krestinskaya, O., James, A.P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.07.2021
• Subjects: Computer architecture; Image edge detection; Logic gates; Memristors; Robot sensing systems; Training; Visualization
• DOI: 10.1109/NANO51122.2021.9514312

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.