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Hyperdimensional computing for sustainable manufacturing: an initial assessment
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AI
DOI：10.1016/j.mfglet.2026.01.004.png)
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Abstract
En 中文
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, 200x for training and 175 to 1000x for inference. Furthermore, HDC reduces training times by 200x and inference times by 300 to 600x , showcasing its potential for energy-efficie nt smart manufacturing. (c) 2026 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Keywords:
Smart Manufacturing
Artificial Intelligence
Hyperdimensional Computing
Sustainability
Energy Efficiency
Journal
M
IF:
2
Papers:
49
Citations:
0