Continual Learning (CL) allows applications such as user personalization and
household robots to learn on the fly and adapt to context. This is an important
feature when context, actions, and users change. However, enabling CL on
resource-constrained embedded systems is challenging due to the limited labeled
data, memory, and computing capacity. In this paper, we propose LifeLearner, a
hardware-aware meta continual learning system that drastically optimizes system
resources (lower memory, latency, energy consumption) while ensuring high
accuracy. Specifically, we (1) exploit meta-learning and rehearsal strategies
to explicitly cope with data scarcity issues and ensure high accuracy, (2)
effectively combine lossless and lossy compression to significantly reduce the
resource requirements of CL and rehearsal samples, and (3) developed
hardware-aware system on embedded and IoT platforms considering the hardware
characteristics. As a result, LifeLearner achieves near-optimal CL performance,
falling short by only 2.8% on accuracy compared to an Oracle baseline. With
respect to the state-of-the-art (SOTA) Meta CL method, LifeLearner drastically
reduces the memory footprint (by 178.7x), end-to-end latency by 80.8-94.2%, and
energy consumption by 80.9-94.2%. In addition, we successfully deployed
LifeLearner on two edge devices and a microcontroller unit, thereby enabling
efficient CL on resource-constrained platforms where it would be impractical to
run SOTA methods and the far-reaching deployment of adaptable CL in a
ubiquitous manner. Code is available at
this https URL

LifeLearner 是一种硬件感知的元可持续学习系统，它在确保高准确性的同时，极大地优化了系统资源（降低内存、延迟和能耗），并成功部署在资源有限的嵌入式平台和物联网设备上，实现了接近最佳的可持续学习性能。