Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing: Background on HDC
2024-7-24 22:0:14 Author: hackernoon.com(查看原文) 阅读量:2 收藏

Authors:

(1) Xiaofan Yu, University of California San Diego, La Jolla, California, USA ([email protected]);

(2) Anthony Thomas, University of California San Diego, La Jolla, California, USA ([email protected]);

(3) Ivannia Gomez Moreno, CETYS University, Campus Tijuana, Tijuana, Mexico ([email protected]);

(4) Louis Gutierrez, University of California San Diego, La Jolla, California, USA ([email protected]);

(5) Tajana Šimunić Rosing, University of California San Diego, La Jolla, USA ([email protected]).

Abstract and 1. Introduction

2 Related Work

3 Background on HDC

4 Problem Definition

5 LifeDH

6 Variants of LifeHD

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.


文章来源: https://hackernoon.com/lifelong-intelligence-beyond-the-edge-using-hyperdimensional-computing-background-on-hdc?source=rss
如有侵权请联系:admin#unsafe.sh