Information Processing Factory 2.0 - Self-awareness for Autonomous Collaborative Systems

The increasing complexity of autonomous vehicle (AV) systems underscores a critical challenge in efficient management of tiered memory architectures, which requires advances beyond conventional memory management strategies to achieve desired performance. This paper introduces MemScape, a novel policy designed to dynamically sculpt the memory landscape of AV systems, facilitating the optimal utilization of tiered memory configurations to minimize unexpected performance drop for AV operation. In contrast to conventional tiered memory management approaches, MemScape utilizes a user- and kernel- level memory allocation flow to migrate memory, which greatly improves system performance, while simultaneously reducing system memory cost. MemScape employs both reactive and proactive reinforcement learning agents for memory management. The reactive agent responds to current performance metrics, while the proactive agent anticipates future memory needs, ensuring optimal performance stability and efficiency. Through comprehensive emulation of an exemplar AV application pipeline utilizing tiered memory, MemScape demonstrates the potential to increase application performance by up to 3.8 × compared to vanilla Linux while saving \(20\% \) system memory cost. Central to MemScape’s efficacy is its novel reactive-and-proactive reinforcement learning approach to memory promotion and demotion that ensures targeting user-level goals. Our findings showcase MemScape’s promise to revolutionize memory management practices in AV systems, presenting a performance-optimized solution adept at meeting the varying demands of emerging autonomous technologies via a combination of reactive and proactive learning strategies.

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Machine learning in run-time control of multicore processor systems

Maurer

Thoma

Surhonne

et al. 2023

It - Information Technology

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Modern embedded and cyber-physical applications consist of critical and non-critical tasks co-located on multiprocessor systems on chip (MPSoCs). Co-location of tasks results in contention for shared resources, resulting in interference on interconnect, processing units, storage, etc. Hence, machine learning-based resource managers must operate even non-critical tasks within certain constraints to ensure proper execution of critical tasks. In this paper we demonstrate and evaluate countermeasures based on backup policies to enhance rule-based reinforcement learning to enforce constraints. Detailed experiments reveal the CPUs’ performance degradation caused by different designs, as well as their effectiveness in preventing constraint violations. Further, we exploit the interpretability of our approach to further improve the resource manager’s operation by adding designers’ experience into the rule set.

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Information Processing Factory 2.0 - Self-awareness for Autonomous Collaborative Systems

Cited by 3 publications

References 22 publications

KDTree-SOM: Self-organizing Map based Anomaly Detection for Lightweight Autonomous Embedded Systems

KDTree-SOM: Self-organizing Map based Anomaly Detection for Lightweight Autonomous Embedded Systems

MemScape: Sculpting Tiered Memory Management for Autonomous Vehicles

Machine learning in run-time control of multicore processor systems

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