Summary of Lifelong Intelligence Beyond the Edge Using Hyperdimensional Computing, by Xiaofan Yu et al.
Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing
by Xiaofan Yu, Anthony Thomas, Ivannia Gomez Moreno, Louis Gutierrez, Tajana Rosing
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary On-device lifelong learning has gained popularity, enabling devices to learn and adapt continuously without relying on cloud-based solutions. This paper introduces LifeHD, a neurally-inspired system designed for general IoT applications with limited supervision. LifeHD employs Hyperdimensional Computing (HDC) and a two-tier associative memory organization to manage high-dimensional vectors representing historical patterns. The authors propose variants of LifeHD to handle scarce labeled inputs and power constraints. Implementing LifeHD on edge platforms, they evaluate its performance across three scenarios, achieving up to 74.8% improvement in unsupervised clustering accuracy compared to state-of-the-art baselines while reducing energy consumption by 34.3x. The system’s code is publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine devices that can learn and adapt on their own without needing to send data to the cloud. This paper creates a new way for devices to do this, called LifeHD. It uses special computer memory to store patterns it has learned and then applies those patterns to new situations. The system is designed for devices with limited power and information, making it perfect for real-world applications like smart homes or cities. The authors tested their system and found that it can learn better than other systems in similar situations while using much less energy. This means LifeHD could be used to create more efficient and effective devices in the future. |
Keywords
* Artificial intelligence * Clustering * Unsupervised
