# Hyperdimensional Computing for Sustainable Manufacturing: An Initial Assessment

arXiv: https://arxiv.org/abs/2512.03864 · HTML: https://mlsanity.com/paper/2512.03864/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 3 of 3)

- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations](https://mlsanity.com/paper/2111.06077/) (2021)
- [Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training](https://mlsanity.com/paper/2208.06102/) (2022)
- [Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training](https://mlsanity.com/paper/2303.02508/) (2023)

## Abstract (arXiv)

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.