Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing: Background on HDC

24 Jul 2024

Authors:

(1) Xiaofan Yu, University of California San Diego, La Jolla, California, USA (x1yu@ucsd.edu);

(2) Anthony Thomas, University of California San Diego, La Jolla, California, USA (ahthomas@ucsd.edu);

(3) Ivannia Gomez Moreno, CETYS University, Campus Tijuana, Tijuana, Mexico (ivannia.gomez@cetys.edu.mx);

(4) Louis Gutierrez, University of California San Diego, La Jolla, California, USA (l8gutierrez@ucsd.edu);

(5) Tajana Šimunić Rosing, University of California San Diego, La Jolla, USA (tajana@ucsd.edu).

Table of Links

Abstract and 1. Introduction

7 Evaluation of LifeHD

8 Evaluation of LifeHD semi and LifeHDa

9 Discussions and Future Works

10 Conclusion, Acknowledgments, and References

3 BACKGROUND ON HDC

Hyperdimensional Computing (HDC) is an emerging paradigm for information processing from the cognitive-neuroscience literature [24]. In HDC, all computation is performed on low-precision and distributed representations of data that accord naturally with highly parallel and low-energy hardware.

Figure 2: Spatiotemporal HDC encoding for time-series data. Left: random generation of level hypervectors. Right: the complete encoding process.

The encoding function 𝜙 : X → H embeds data from its ambient representation into HD-space. In general, encoding should preserve some meaningful notion of similarity between input points in the sense that 𝜙 (𝑥) · 𝜙 (𝑥 ′ ) ≈ 𝑘 (𝑥, 𝑥′ ), where 𝑘 is some similarity function of interest on X. In this paper, we use spatiotemporal encoding for time series sensor data, and HDnn for more complex data, such as images, which we explain in the following.

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.