On Hardware-Aware Design and Optimization of Edge Intelligence
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Abstract (arXiv)
Edge intelligence systems, the intersection of edge computing and artificial intelligence (AI), are pushing the frontier of AI applications. However, the complexity of deep learning models and heterogeneity of edge devices make the design of edge intelligence systems a challenging task. Hardware-agnostic methods face some limitations when implementing edge systems. Thus, hardware-aware methods are attracting more attention recently. In this paper, we present our recent endeavors in hardware-aware design and optimization for edge intelligence. We delve into techniques such as model compression and neural architecture search to achieve efficient and effective system designs. We also discuss some challenges in hardware-aware paradigm.
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References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
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- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
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- 2019Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligencecited by 76
- 2020Bringing AI To Edge: From Deep Learning's Perspectivecited by 24
- 2026Smart Scissor: Coupling Spatial Redundancy Reduction and CNN Compression for Embedded Hardwarecited by 3
- 2026Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systemscited by 1
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