Hyperdimensional Computing for Sustainable Manufacturing: An Initial Assessment

2025/12/03 by Danny Hoang, Anandkumar Patel, Hoang, Danny +7
Computer Science · Engineering · #Advanced Memory and Neural Computing #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Ferroelectric and Negative Capacitance Devices #Machine Learning (cs.LG) #Parallel Computing and Optimization Techniques #Performance (cs.PF) #Symbolic Computation (cs.SC)

paper · pdf · doi:10.48550/arxiv.2512.03864

openalex publication_date 2025/12/03 · openalex created_date 2025/12/05 · openalex updated_date 2026/07/28

Abstract

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× for training and 175 to 1000× for inference. Furthermore, HDC reduces training times by 200× and inference times by 300 to 600×, showcasing its potential for energy-efficient smart manufacturing.

Citations

• Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training
• Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training
• A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations

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