Abstract
Artificial intelligence and machine learning models deployed on edge devices, e.g., for quality control in Additive Manufacturing (AM), are frequently small in size. Such models usually have to deliver highly accurate results within a short time frame. Methods that are commonly employed in literature start out with larger trained models and try to reduce their memory and latency footprint by structural pruning, knowledge distillation, or quantization. It is, however, also possible to leverage hardware-aware Neural Architecture Search (NAS), an approach that seeks to systematically explore the architecture space to find optimized configurations. In this study, a hardware-aware NAS workflow is introduced that couples an edge device located in Belgium with a powerful High-Performance Computing system in Germany, to train possible architecture candidates as fast as possible while performing real-time latency measurements on the target hardware. The approach is verified on a use case in the AM domain, based on the open RAISE-LPBF dataset, achieving ~8.8 times faster inference speed while simultaneously enhancing model quality by a factor of ~1.35, compared to a human-designed baseline.
Abstract (translated)
部署在边缘设备上的人工智能和机器学习模型（例如用于增材制造中的质量控制）通常尺寸较小。这些模型需要在短时间内提供高度准确的结果。文献中常用的方法是从较大的预训练模型开始，通过结构化剪枝、知识蒸馏或量化来减少其内存占用量和延迟。 然而，也可以利用硬件感知的神经架构搜索（NAS）方法，这是一种系统地探索架构空间以找到优化配置的方法。在本研究中，介绍了一种结合了比利时边缘设备与德国强大的高性能计算系统的硬件感知NAS工作流程，旨在尽可能快速训练可能的架构候选模型，并同时在目标硬件上进行实时延迟测量。这种方法在一个基于开放的RAISE-LPBF数据集的实际增材制造案例中得到了验证，在比人类设计的基础线方案快约8.8倍的同时，还将模型质量提升了大约1.35倍。 简单来说，这项研究通过结合边缘设备和高性能计算资源开发了一种新的硬件感知神经架构搜索方法。这种方法能够在保持或提高模型性能的同时显著提升推理速度，这对于诸如增材制造中的质量控制等应用场景非常有价值。
URL
https://arxiv.org/abs/2505.19995
