MonTM: Monitoring-Based Thermal Management for Mixed-Criticality Systems

With a rapidly growing functionality of embedded real-time applications, it becomes inevitable to integrate tasks of different safety integrity levels on one many-core processor leading to a large-scale mixed-criticality system. In this process, it is not sufficient to only isolate shared architectural resources, as different tasks executing on different cores also possibly interfere via the many-core processor’s thermal management. This can possibly lead to best-effort tasks causing deadline violations for safety-critical tasks. In order to prevent such a scenario, we propose a monitoring-based hardware extension that communicates imminent thermal violations between cores via a lightweight interconnect. Building on this infrastructure, we propose a thermal strategy such that best-effort tasks can be throttled in favor of safety-critical tasks. Furthermore, assigning static voltage/frequency (V/f) levels to each safety-critical task based on their worst-case execution time may result in unnecessary high V/f levels when the actual execution finishes faster. To free the otherwise wasted thermal resources, our solution monitors the progress of safety-critical tasks to detect slack and safely reduce their V/f levels. This increases the thermal headroom for best-effort tasks, boosting their performance. In our evaluation, we demonstrate our approach on an 80-core processor to show that it satisfies the thermal and deadline requirements, and simultaneously reduces the run-time of best-effort tasks by up to 45% compared to the state of the art

Hybrid Application Mapping for Composable Many-Core Systems: Overview and Future Perspective

Many-core platforms are rapidly expanding in various embedded areas as they provide the scalable computational power required to meet the ever-growing performance demands of embedded applications and systems. However, the huge design space of possible task mappings, the unpredictable workload dynamism, and the numerous non-functional requirements of applications in terms of timing, reliability, safety, and so forth. impose significant challenges when designing many-core systems. Hybrid Application Mapping (HAM) is an emerging class of design methodologies for many-core systems which address these challenges via an incremental (per-application) mapping scheme: The mapping process is divided into (i) a design-time Design Space Exploration (DSE) step per application to obtain a set of high-quality mapping options and (ii) a run-time system management step in which applications are launched dynamically (on demand) using the precomputed mappings. This paper provides an overview of HAM and the design methodologies developed in line with it. We introduce the basics of HAM and elaborate on the way it addresses the major challenges of application mapping in many-core systems. We provide an overview of the main challenges encountered when employing HAM and survey a collection of state-of-the-art techniques and methodologies proposed to address these challenges. We finally present an overview of open topics and challenges in HAM, provide a summary of emerging trends for addressing them particularly using machine learning, and outline possible future directions. While there exists a large body of HAM methodologies, the techniques studied in this paper are developed, to a large extent, within the scope of invasive computing. Invasive computing introduces resource awareness into applications and employs explicit resource reservation to enable incremental application mapping and dynamic system management