A 28-nm 25.1 TOPS/W Sparsity-Aware CNN-GCN Deep Learning SoC for Mobile Augmented Reality.
2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)(2022)
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.
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Key words
mobile augmented reality,CNN-GCN SoC,diverse AI vision computations,CNN engine,channel-wise feature sparsity,specialized processing element,6.1× energy efficiency,GCN engine,graph-based action recognition,computational complexity,memory usage,matrix,graph properties,CNN inference
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