Small devices are frequently used in IoT and smart-city applications to
perform periodic dedicated tasks with soft deadlines. This work focuses on
developing methods to derive efficient power-management methods for periodic
tasks on small devices. We first study the limitations of the existing Linux
built-in methods used in small devices. We illustrate three typical
workload/system patterns that are challenging to manage with Linux's built-in
solutions. We develop a reinforcement-learning-based technique with temporal
encoding to derive an effective DVFS governor even with the presence of the
three system patterns. The derived governor uses only one performance counter,
the same as the built-in Linux mechanism, and does not require an explicit task
model for the workload. We implemented a prototype system on the Nvidia Jetson
Nano Board and experimented with it with six applications, including two
self-designed and four benchmark applications. Under different deadline
constraints, our approach can quickly derive a DVFS governor that can adapt to
performance requirements and outperform the built-in Linux approach in energy
saving. On Mibench workloads, with performance slack ranging from 0.04 s to 0.4
s, the proposed method can save 3% - 11% more energy compared to Ondemand.
AudioReg and FaceReg applications tested have 5%- 14% energy-saving
improvement. We have open-sourced the implementation of our in-kernel quantized
neural network engine. The codebase can be found at:
this https URL

在本研究中，我们专注于为小型设备开发高效的功耗管理方法，通过使用基于强化学习和时间编码的技术从三种具有挑战性的工作负载 / 系统模式中导出一个有效的 DVFS 调度器，该调度器仅使用一个性能计数器并能适应性能需求，在能源节约方面优于内置的 Linux 方法。

基于时间编码的深度强化学习在嵌入式设备上的实时应用 CPU 频率调度

CPU frequency scheduling of real-time applications on embedded devices  with temporal encoding-based deep reinforcement learning

We consider the problem of performing Spoken Language Understanding (SLU) on
small devices typical of IoT applications. Our contributions are twofold.
First, we outline the design of an embedded, private-by-design SLU system and
show that it has performance on par with cloud-based commercial solutions.
Second, we release the datasets used in our experiments in the interest of
reproducibility and in the hope that they can prove useful to the SLU
community.

本文介绍了一种嵌入式的、面向小型物联网应用的 Spoken Language Understanding 系统，并证明其性能可以与基于云的商业解决方案相媲美。同时，我们还提供了我们实验使用的数据集，旨在促进 SLU 社区的可重复性和有益性。