论边缘智能的硬件感知设计与优化
On Hardware-Aware Design and Optimization of Edge Intelligence
浏览论文内容
中文总结 AI 辅助
研究边缘智能系统设计难题，通过深入探究模型压缩和神经架构搜索等硬件感知技术来实现高效设计，并探讨了该范式面临的挑战。
中文摘要 AI 辅助
边缘智能系统作为边缘计算与人工智能的交叉领域，正推动着人工智能应用的前沿发展。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设计成为一项具有挑战性的任务。硬件无关方法在实现边缘系统时面临一些限制。因此，硬件感知方法最近受到了更多关注。本文介绍了我们在边缘智能的硬件感知设计和优化方面的最新努力。我们深入研究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。我们还讨论了硬件感知范式中的一些挑战。
英文摘要
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.
发表机构
- School of Computer Science and Engineering, Nanyang Technological University, Singapore（南洋理工大学计算机科学与工程学院）
- HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University, Singapore（南洋理工大学HP-NTU数字制造企业实验室）
- Department of Computer Science, Norwegian University of Science and Technology, Norway（挪威技术大学计算机科学系）
- HP Inc., Palo Alto, California, USA（惠普公司）
机构由 AI 辅助整理，请以论文原文为准。
