论文标题
消费者边缘计算的未来
The Future of Consumer Edge-AI Computing
论文作者
论文摘要
在过去的十年中，深度学习迅速渗透了消费者的终点，这主要归功于跨设备的硬件加速。但是，随着我们的未来，很明显，孤立的硬件将不足。日益复杂的AI任务需要共享资源，跨设备协作和多种数据类型，而不会损害用户隐私或经验质量。为了解决这个问题，我们引入了一个以Edgeai-hub设备为中心的新颖范式，旨在重组和优化消费者边缘的计算资源和数据访问。为此，我们为在消费者环境中从设备到边缘服务系统的过渡奠定了整体基础，详细介绍了它们的组件，结构，挑战和机遇。
In the last decade, Deep Learning has rapidly infiltrated the consumer end, mainly thanks to hardware acceleration across devices. However, as we look towards the future, it is evident that isolated hardware will be insufficient. Increasingly complex AI tasks demand shared resources, cross-device collaboration, and multiple data types, all without compromising user privacy or quality of experience. To address this, we introduce a novel paradigm centered around EdgeAI-Hub devices, designed to reorganise and optimise compute resources and data access at the consumer edge. To this end, we lay a holistic foundation for the transition from on-device to Edge-AI serving systems in consumer environments, detailing their components, structure, challenges and opportunities.
