Energy-centric design is paramount in the current embedded computing era: use cases require increasingly high performance at an affordable power budget, often under real-time constraints. Hardware heterogeneity and parallelism help address the efficiency challenge, but greatly complicate online power consumption assessments, which are essential for dynamic hardware and software stack adaptations. We introduce a novel power modeling methodology with state-of-the-art accuracy, low overhead, and high responsiveness, whose implementation does not rely on microarchitectural details. Our methodology identifies the Performance Monitoring Counters (PMCs) with the highest linear correlation to the power consumption of each hardware sub-system, for each Dynamic Voltage and Frequency Scaling (DVFS) state. The individual, simple models are composed into a complete model that effectively describes the power consumption of the whole system, achieving high accuracy and low overhead. Our evaluation reports an average estimation error of 7.5% for power consumption and 1.3% for energy. We integrate these models in the Linux kernel with Runmeter, an open-source, PMC-based monitoring framework. Runmeter manages PMC sampling and processing, enabling the execution of our power models at runtime. With a worst-case time overhead of only 0.7%, Runmeter provides responsive and accurate power measurements directly in the kernel. This information can be employed for actuation policies in workload-aware DVFS and power-aware, closed-loop task scheduling.

Venue-specific training has, over the years, proven to be a key asset for sailing teams performing at major championships and at the Olympics. A comprehensive understanding of the environmental features and possible weather scenarios in an outdoor sport like sailing can provide athletes with a significant strategic advantage. At the same time, GPS tracking is becoming a readily available technology that athletes use both in training and during races to analyse their own and their competitor’s performance. This work couples environmental and meteorological data with GPS tracks for Olympic sailing classes, linking weather features with strategic decisions on the race track. We propose a greedy algorithm to search for an optimal route based on weather forecasts to present the best strategy prior to the race. Our results show the potential of this approach to provide valuable decision support for athletes in Olympic sailing competitions, demonstrated for the 470 Olympic class.

There is an evident need to complement embedded critical control logic with AI inference, but today’s AI-capable hardware, software, and processes are primarily targeted towards the needs of cloud-centric actors. Telecom and defense airspace industries, which make heavy use of specialized hardware, face the challenge of manually hand-tuning AI workloads and hardware, presenting an unprecedented cost and complexity due to the diversity and sheer number of deployed instances. Furthermore, embedded AI functionality must not adversely affect real-time and safety requirements of the critical business logic. To address this, end-to-end AI pipelines for critical platforms are needed to automate the adaption of networks to fit into resource-constrained devices under critical and real-time constraints, while remaining interoperable with de-facto standard AI tools and frameworks used in the cloud. We present two industrial applications where such solutions are needed to bring AI to critical and resource-constrained hardware, and a generalized end-to-end AI pipeline that addresses these needs. Crucial steps to realize it are taken in the industry-academia collaborative FASTER-AI project. © 2024 EDAA.