Few-Shot Class-Incremental Learning (FSCIL) enables machine learning systems
to expand their inference capabilities to new classes using only a few labeled
examples, without forgetting the previously learned classes. Classical
backpropagation-based learning and its variants are often unsuitable for
battery-powered, memory-constrained systems at the extreme edge. In this work,
we introduce Online Few-Shot Class-Incremental Learning (O-FSCIL), based on a
lightweight model consisting of a pretrained and metalearned feature extractor
and an expandable explicit memory storing the class prototypes. The
architecture is pretrained with a novel feature orthogonality regularization
and metalearned with a multi-margin loss. For learning a new class, our
approach extends the explicit memory with novel class prototypes, while the
remaining architecture is kept frozen. This allows learning previously unseen
classes based on only a few examples with one single pass (hence online).
O-FSCIL obtains an average accuracy of 68.62% on the FSCIL CIFAR100 benchmark,
achieving state-of-the-art results. Tailored for ultra-low-power platforms, we
implement O-FSCIL on the 60 mW GAP9 microcontroller, demonstrating online
learning capabilities within just 12 mJ per new class.

本研究介绍了一种在线少样本增量学习（O-FSCIL）方法，该方法通过使用预训练和元学习的特征提取器以及存储类别原型的可扩展显式内存，使机器学习系统能够仅使用少量标记示例来扩展其推理能力，同时不会忘记先前学习的类别。O-FSCIL 在 FSCIL CIFAR100 基准测试中获得了 68.62％的平均准确率，实现了最先进的结果。针对超低功耗平台，我们将 O-FSCIL 实现在 60 mW 的 GAP9 微控制器上，仅使用 12 mJ 的能量来展示在线学习能力。

设备上每类 12 毫焦耳的在线少样本分类 - 增量学习

12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning

Advances in Tiny Machine Learning (TinyML) have bolstered the creation of
smart industry solutions, including smart agriculture, healthcare and smart
cities. Whilst related research contributes to enabling TinyML solutions on
constrained hardware, there is a need to amplify real-world applications by
optimising energy consumption in battery-powered systems. The work presented
extends and contributes to TinyML research by optimising battery-powered
image-based anomaly detection Internet of Things (IoT) systems. Whilst previous
work in this area has yielded the capabilities of on-device inferencing and
training, there has yet to be an investigation into optimising the management
of such capabilities using machine learning approaches, such as Reinforcement
Learning (RL), to improve the deployment battery life of such systems. Using
modelled simulations, the battery life effects of an RL algorithm are
benchmarked against static and dynamic optimisation approaches, with the
foundation laid for a hardware benchmark to follow. It is shown that using RL
within a TinyML-enabled IoT system to optimise the system operations, including
cloud anomaly processing and on-device training, yields an improved battery
life of 22.86% and 10.86% compared to static and dynamic optimisation
approaches respectively. The proposed solution can be deployed to
resource-constrained hardware, given its low memory footprint of 800 B, which
could be further reduced. This further facilitates the real-world deployment of
such systems, including key sectors such as smart agriculture.

通过使用强化学习算法优化系统操作和云异常处理，TinyML 启用的物联网系统在性能优化方面获得了较好的结果，相对于静态和动态优化方法分别提高了 22.86％和 10.86％的电池寿命。