Table of Links

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

10 CONCLUSION

The ability to learn continuously and indefinitely in the presence of change, and without access to supervision, on a resource-constrained device is a crucial trait for future sensor systems. In this work, we design and deploy the first end-to-end system named LifeHD to learn continuously from real-world data streams without labels. Our approach is based on Hyperdimensional Computing (HDC), an emerging neurally-inspired paradigm for lightweight edge computing. LifeHD is built on a two-tier memory hierarchy including a working and a long-term memory, with collaborative components of novelty detection, online cluster HV update and cluster HV merging for optimal lifelong learning performance. We further propose two extensions to LifeHD, LifeHDsemi and LifeHDa, to handle scarce labeled samples and power constraints. Practical deployments on typical edge platforms and three IoT scenarios demonstrate LifeHD’s improvement of up to 74.8% on unsupervised clustering accuracy and up to 34.3x on energy efficiency compared to state-of-the-art NN-based unsupervised lifelong learning baselines [13, 14, 54].

ACKNOWLEDGMENTS

The authors would like to thank the anonymous shepherd, reviewers, and our colleague Xiyuan Zhang for their valuable feedback. This work was supported in part by National Science Foundation under Grants #2003279, #1826967, #2100237, #2112167, #1911095, #2112665, and in part by PRISM and CoCoSys, centers in JUMP 2.0, an SRC program sponsored by DARPA.

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