| dc.description.abstract | Deploying AI models on edge devices presents challenges in ensuring reliable and
energy-efficient operations. Edge AI processes data directly on devices like IoT sensors
and industrial machinery, enabling real-time decision-making and reducing latency,
which is crucial for applications such as autonomous driving and robotics. However,
deploying these models often involves custom Infrastructure as Code (IaC) scripts,
and a lack of reproducibility in these scripts can cause inconsistencies and affect
system reliability. Additionally, while advancements in hardware like SoCs, FPGAs,
and AI accelerators have improved Edge AI capabilities, these deployments can lead
to high energy consumption.
Our research addresses these challenges through two main contributions. First, we
identify and categorize reproducibility smells in IaC scripts, particularly focusing on
an automation platform, Ansible, that allows imperative infrastructure configuration.
We developed a tool, Reduse, to detect these reproducibility smells, in the pursuit
to ensure that IaC scripts are reliable and consistent. Our empirical study reveals
the occurrence of these smells in open-source projects, with significant correlations
and co-occurrence patterns among them. For instance, the broken dependency chain
smell was found in approximately 71% of Ansible tasks analyzed, highlighting common
reproducibility issues.
Second, we comprehensively evaluate the selection of AI models on edge devices,
including the Raspberry Pi, NVIDIA Jetson Nano, and Intel Neural Compute Stick.
By measuring inference power consumption, accuracy, inference time, and memory
utilization, we offer insights into the performance and energy efficiency trade-offs of
these models. For instance, Jetson Nano provides the best accuracy at the cost of
a high energy budget. Thus, our work advances the field of edge AI with the best
practices in IaC, contributing to more reliable and effective AI deployments in real-
world scenarios. | en_US |
