针对边缘设备异构性，提出软硬件协同优化的智能系统设计方法。
On Hardware-Aware Design and Optimization of Edge Intelligence
- 结合模型压缩与神经架构搜索实现高效部署
- 解决深度模型复杂度与硬件多样性之间的矛盾
- 适合边缘AI系统研发人员参考
边缘智能系统是边缘计算与人工智能（AI）的交叉领域，正推动AI应用的前沿发展。然而，深度学习模型的复杂性和边缘设备的异构性使得边缘智能系统的构建面临挑战。传统的硬件无关方法在实际部署中存在局限性，因此硬件感知方法近年来受到更多关注。本文介绍了我们在边缘智能系统中硬件感知设计与优化方面的最新研究进展，深入探讨了模型压缩和神经架构搜索等技术，以实现高效且有效的系统设计。同时，我们还讨论了硬件感知范式中面临的一些关键挑战。
原文摘要 · Abstract (English)
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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