학술논문
A 28-nm 25.1 TOPS/W Sparsity-Aware CNN-GCN Deep Learning SoC for Mobile Augmented Reality
Document Type
Conference
Author
Source
2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits) VLSI Technology and Circuits (VLSI Technology and Circuits), 2022 IEEE Symposium on. :42-43 Jun, 2022
Subject
Language
ISSN
2158-9682
Abstract
This work presents the first CNN-GCN SoC for diverse AI vision computations on mobile augmented reality (AR). A CNN engine utilizes the channel-wise feature sparsity with a specialized processing element to achieve an up to 8× higher throughput and 6.1× energy efficiency. A GCN engine is implemented for graph-based action recognition. The computational complexity and memory usage are minimized by lever-aging matrix and graph properties. The proposed SoC achieves 25.1 TOPS/W energy efficiency for CNN inference, outperforming prior designs by 2.0×. It delivers 72 action/s on action recognition, exceeding prior art by 18× in latency.