  On-device learning has emerged as a prevailing trend that avoids the slow
response time and costly communication of cloud-based learning. The ability to
learn continuously and indefinitely in a changing environment, and with
resource constraints, is critical for real sensor deployments. However,
existing designs are inadequate for practical scenarios with (i) streaming data
input, (ii) lack of supervision and (iii) limited on-board resources. In this
paper, we design and deploy the first on-device lifelong learning system called
LifeHD for general IoT applications with limited supervision. LifeHD is
designed based on a novel neurally-inspired and lightweight learning paradigm
called Hyperdimensional Computing (HDC). We utilize a two-tier associative
memory organization to intelligently store and manage high-dimensional,
low-precision vectors, which represent the historical patterns as cluster
centroids. We additionally propose two variants of LifeHD to cope with scarce
labeled inputs and power constraints. We implement LifeHD on off-the-shelf edge
platforms and perform extensive evaluations across three scenarios. Our
measurements show that LifeHD improves the unsupervised clustering accuracy by
up to 74.8% compared to the state-of-the-art NN-based unsupervised lifelong
learning baselines with as much as 34.3x better energy efficiency. Our code is
available at https://github.com/Orienfish/LifeHD.
