Smart manufacturing can significantly improve efficiency and reduce energy consumption, yet the energy demands of AI models may offset these gains. This study utilizes in-situ sensing-based prediction of geometric quality in smart machining to compare the energy consumption, accuracy, and speed of common AI models. HyperDimensional Computing (HDC) is introduced as an alternative, achieving accuracy comparable to conventional models while drastically reducing energy consumption, 200$\times$ for training and 175 to 1000$\times$ for inference. Furthermore, HDC reduces training times by 200$\times$ and inference times by 300 to 600$\times$, showcasing its potential for energy-efficient smart manufacturing.

翻译：智能制造能显著提升效率并降低能耗，但人工智能模型的能源需求可能抵消这些收益。本研究基于智能加工中的原位传感几何质量预测，比较了常见人工智能模型的能耗、精度和速度。超维计算被引入作为一种替代方案，在实现与传统模型相当精度的同时，大幅降低了能耗——训练能耗降低200倍，推理能耗降低175至1000倍。此外，超维计算将训练时间缩短200倍，推理时间缩短300至600倍，展示了其在节能智能制造中的潜力。