Convolutional neural networks

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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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EPU: An Energy-Efficient Explainable AI Accelerator With Sparsity-Free Computation and Heat Map Compression/Pruning
EPU: An Energy-Efficient Explainable AI Accelerator With Sparsity-Free Computation and Heat Map Compression/Pruning 150 150
Author(s): Junsoo Kim, Seunghee Han, Geonwoo Ko, Ji-Hoon Kim, Changha Lee, Taewoo Kim, Chan-Hyun Youn, Joo-Young Kim

Abstract:

Deep neural networks (DNNs) have recently gained significant prominence in various real-world applications such as image recognition, natural language processing, and autonomous vehicles. However, due to their black-box nature in system, the underlying mechanisms of DNNs behind the inference results remain opaque to users. In order to address this challenge, …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 830 – 841
Year of Publication: 2024
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2023.3346913
Publisher: IEEE

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