Network delays, throughput bottlenecks and privacy issues push Artificial
Intelligence of Things (AIoT) designers towards evaluating the feasibility of
moving model training and execution (inference) as near as possible to the
terminals. Meanwhile, results from the TinyML community demonstrate that, in
some cases, it is possible to execute model inference directly on the terminals
themselves, even if these are small microcontroller-based devices. However, to
date, researchers and practitioners in the domain lack convenient all-in-one
toolkits to help them evaluate the feasibility of moving execution of arbitrary
models to arbitrary low-power IoT hardware. To this effect, we present in this
paper U-TOE, a universal toolkit we designed to facilitate the task of AIoT
designers and researchers, by combining functionalities from a low-power
embedded OS, a generic model transpiler and compiler, an integrated performance
measurement module, and an open-access remote IoT testbed. We provide an open
source implementation of U-TOE and we demonstrate its use to experimentally
evaluate the performance of a wide variety of models, on a wide variety of
low-power boards, based on popular microcontroller architectures (ARM Cortex-M
and RISC-V). U-TOE thus allows easily reproducible and customisable comparative
evaluation experiments in this domain, on a wide variety of IoT hardware
all-at-once. The availability of a toolkit such as U-TOE is desirable to
accelerate the field of AIoT, towards fully exploiting the potential of edge
computing.

本文介绍了一种名为 U-TOE 的通用工具包，它结合了低功耗嵌入式操作系统、通用模型转换器和编译器、集成性能测量模块以及开放访问的远程 IoT 测试平台的功能，旨在为 AIoT 设计师和研究人员提供便利，加速边缘计算的发展。