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A 25.1-TOPS/W Sparsity-Aware Hybrid CNN-GCN Deep Learning SoC for Mobile Augmented Reality
A 25.1-TOPS/W Sparsity-Aware Hybrid CNN-GCN Deep Learning SoC for Mobile Augmented Reality 150 150
Author(s): Wen-Cong Huang, I-Ting Lin, Ying-Sheng Lin, Wen-Ching Chen, Liang-Yi Lin, Nian-Shyang Chang, Chun-Pin Lin, Chi-Shi Chen, Chia-Hsiang Yang

Abstract:

Augmented reality (AR) has been applied to various mobile applications. Modern AR algorithms include neural networks, such as convolutional neural networks (CNNs) and graph convolutional networks (GCNs). The high computational complexity of these networks poses challenges for real-time operation on energy-constrained devices. This article presents the first energy-efficient hybrid CNN-GCN …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2024
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2024.3391665
Publisher: IEEE

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A Dynamic Execution Neural Network Processor for Fine-Grained Mixed-Precision Model Training Based on Online Quantization Sensitivity Analysis
A Dynamic Execution Neural Network Processor for Fine-Grained Mixed-Precision Model Training Based on Online Quantization Sensitivity Analysis 150 150
Author(s): Ruoyang Liu, Chenhan Wei, Yixiong Yang, Wenxun Wang, Binbin Yuan, Huazhong Yang, Yongpan Liu

Abstract:

BSTcontrol As neural network (NN) training cost red has been growing exponentially over the past decade, developing high-speed and energy-efficient training methods has become an urgent task. Fine-grained mixed-precision low-bit training is the most promising way for high-efficiency training, but it needs dedicated processor designs to overcome the overhead in …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 12
Year of Publication: 2024
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2024.3377292
Publisher: IEEE

View on IEEE Xplore

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