Edge computing empowered Deep Learning (DL) solutions have risen as the foremost facilitators of automation in a multitude of smart manufacturing applications. These models are implemented on edge devices with frozen learning capabilities to execute DL inference task(s). Nevertheless, the data they process is susceptible to intermittent alterations amidst the ever-changing landscape of dynamic smart manufacturing ecosystem. It sparks the demand for model maintenance solution(s) to address adaptability and dynamism issues to enhance the efficiency of smart manufacturing solutions. Moreover, additional issue(s), such as the non-availability of comprehensive data (or the availability of solely contemporary data), near-real-time execution of DL model maintenance task, etc., imposes daunting obstructions in devising an efficient DL model maintenance strategy. This work proposes a novel approach that encompasses the merits of Continual Learning (CL) and Split Learning (SL) driven by edge intelligence, amalgamating them into a hybrid solution aptly named Split-based Continual Learning (SCL). CL ensures the sustained performance of the DL model amidst constraints related to data availability. At the same time, SL empowers near-real-time execution at the edge to achieve improved efficiency. An extension of the SCL scheme, termed as Extended SCL (ESCL), is implemented to addresses the interaction soundness aspects among the mobile edge devices in a collaborative execution environment. Evaluation of a vision-based product-quality inspection use case in an emulated hardware test-bed setup signifies that the performance of SCL and ESCL schemes have the potential to meet the needs of smart manufacturing. SCL attains an appreciable reduction in the model maintenance cost in the range of 21 to 48 and 12 to 29 percent compared to the ECN-only and basic-SL schemes. The ESCL scheme further improved the performance by 18 to 34 and 20 to 36 percent respectively over the basic-SL and SCL.

中文翻译：

---

SCL：智能制造中边缘计算生态系统的可持续深度学习解决方案


边缘计算支持的深度学习 （DL） 解决方案已成为众多智能制造应用中自动化的最重要推动者。这些模型在具有冻结学习能力的边缘设备上实现，以执行 DL 推理任务。然而，在不断变化的动态智能制造生态系统环境中，他们处理的数据很容易受到间歇性变化的影响。它激发了对模型维护解决方案的需求，以解决适应性和动态性问题，以提高智能制造解决方案的效率。此外，其他问题，例如无法获得全面的数据（或仅提供当代数据）、DL 模型维护任务的近乎实时的执行等，为制定有效的 DL 模型维护策略带来了令人生畏的障碍。这项工作提出了一种新颖的方法，该方法结合了由边缘智能驱动的持续学习 （CL） 和拆分学习 （SL） 的优点，将它们合并成一个混合解决方案，恰如其分地命名为基于拆分的持续学习 （SCL）。CL 可确保 DL 模型在与数据可用性相关的约束下保持持续性能。同时，SL 支持在边缘进行近乎实时的执行，以提高效率。SCL 方案的扩展，称为扩展 SCL （ESCL），旨在解决协同执行环境中移动边缘设备之间的交互健全性问题。在仿真硬件测试台设置中对基于视觉的产品质量检测用例进行评估，表明 SCL 和 ESCL 方案的性能有可能满足智能制造的需求。 与仅 ECN 和基本 SL 方案相比，标准及校正实验所的模型维护成本显著降低了 21% 至 48% 和 12% 至 29%。与 basic-SL 和 SCL 相比，ESCL 方案进一步将性能分别提高了 18% 至 34% 和 20% 至 36%。