DT-CNN: Dilated and Transposed Convolution Neural Network Accelerator for Real-Time Image Segmentation on Mobile Devices

• Published in: 2019 IEEE International Symposium on Circuits and Systems (ISCAS) pp. 1 - 5
• Main Authors: Im, Dongseok, Han, Donghyeon, Choi, Sungpill, Kang, Sanghoon, Yoo, Hoi-Jun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2019
• Subjects: action recognition; Computer architecture; Convolution; convolution neural network; Deep neural network; Delays; dilated convolution; human body segmentation; Image segmentation; Kernel; Microprocessors; Throughput; transposed convolution
• ISBN: 9781728103976; 1728103975
• ISSN: 2158-1525
• DOI: 10.1109/ISCAS.2019.8702243

A convolution neural network (CNN) accelerator is proposed for real-time image segmentation on mobile devices. The proposed CNN processor cuts down the redundant zero computations in dilated and transposed convolution for higher throughput. As a result, the overall computations of the image segmentation are reduced by 86.6% and the proposed CNN processor boosts up the throughput 6.7×. Moreover, the proposed processor utilizes RoI (Region of Interest) based image segmentation algorithm to reduce the overall computational requirement significantly. Although RoI based image segmentation degrades the image segmentation accuracy, the proposed dilation rate adjustment compensates for the accuracy degradation and maintains the accuracy of the full-size image segmentation. Finally, the proposed CNN processor is simulated in 65 nm CMOS technology, and it occupies 6.8 mm 2 . The proposed processor consumes 196 mW and shows 211 frames-per-second (fps) at the image segmentation for human body parts.