Global Adaptation for Energy Efficiency in Multicore Architectures

Lenz, Alina; Pieper, Tobias; Obermaisser, Roman

doi:10.1109/pdp.2017.46

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…The scheduling decisions were made a priori in the meta-scheduler, where the subset of reachable schedules was generated. The simulation shows that the interactive consistency protocol helps to save energy [1].…”

Section: Synthetic Use Casementioning

confidence: 94%

“…At runtime, the difference between the worst case and the real system state can be exploited for the benefit of the system. Using a meta-scheduler [1], a set of schedules can be computed at design time, between which the system can switch at runtime. A so-called meta-scheduler is an application that computes a directed acyclic graph (DAG) of schedules that can be reached at runtime.…”

Section: Introductionmentioning

confidence: 99%

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Global Adaptation Controlled by an Interactive Consistency Protocol

Lenz

Obermaisser

2017

JLPEA

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Static schedules for systems can lead to an inefficient usage of the resources, because the system's behavior cannot be adapted at runtime. To improve the runtime system performance in current time-triggered Multi-Processor System on Chip (MPSoC), a dynamic reaction to events is performed locally on the cores. The effects of this optimization can be increased by coordinating the changes globally. To perform such global changes, a consistent view on the system state is needed, on which to base the adaptation decisions. This paper proposes such an interactive consistency protocol with low impact on the system w.r.t. latency and overhead. We show that an energy optimizing adaptation controlled by the protocol can enable a system to save up to 43% compared to a system without adaptation.

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Section: Synthetic Use Casementioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Global Adaptation Controlled by an Interactive Consistency Protocol

Lenz

Obermaisser

2017

JLPEA

Self Cite

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“…In turn, computing a schedule for every context event increases the size of the MSG exponentially. With reconvergence of paths through a fixed reconvergence horizon, this relationship is polynomial [8].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Scheduling for Time-Triggered Network-on-Chip-Based Multi-Core Architecture Using Genetic Algorithm

2021

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Adaptation in time-triggered systems can be motivated by energy efficiency, fault recovery, and changing environmental conditions. Adaptation in time-triggered systems is achieved by preserving temporal predictability through metascheduling techniques. Nevertheless, utilising existing metascheduling schemes for time-triggered network-on-chip architectures poses design time computation and run-time storage challenges for adaptation using the resulting schedules. In this work, an algorithm for path reconvergence in a multi-schedule graph, enabled by a reconvergence horizon, is presented to manage the state-space explosion problem resulting from an increase in the number of scenarios required for adaptation. A meta-scheduler invokes a genetic algorithm to solve a new scheduling problem for each adaptation scenario, resulting in a multi-schedule graph. Finally, repeated nodes of the multi-schedule graph are merged, and further exploration of paths is terminated. The proposed algorithm is evaluated using various application model sizes and different horizon configurations. Results show up to 56% reduction of schedules necessary for adaptation to 10 context events, with the reconvergence horizon set to 50 time units. Furthermore, 10 jobs with 10 slack events and a horizon of 40 ticks result in a 23% average sleep time for energy savings. Furthermore, the results demonstrate the reduction in the state-space size while showing the trade-off between the size of the reconvergence horizon and the number of nodes of the multi-schedule graph.

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“…The scheduling algorithm for computing the schedules needs to avoid state explosion. For example, enforcing reconvergence horizons for context events prevents an exponential growth of the number of schedules with increasing numbers of context events [19].…”

Section: Avoidance Of State Explosionmentioning

confidence: 99%

“…Without reconvergence, there would be an exponential relationship between the number of events in the context model and the schedule size. With a constant reconvergence horizon, there is a polynomial dependency between the number of events and the size of the MG [19].…”

mentioning

confidence: 99%

Adaptive Time-Triggered Multi-Core Architecture

Obermaisser

Ahmadian

Maleki

et al. 2019

Designs

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The static resource allocation in time-triggered systems offers significant benefits for the safety arguments of dependable systems. However, adaptation is a key factor for energy efficiency and fault recovery in Cyber-Physical System (CPS). This paper introduces the Adaptive Time-Triggered Multi-Core Architecture (ATMA), which supports adaptation using multi-schedule graphs while preserving the key properties of time-triggered systems including implicit synchronization, temporal predictability and avoidance of resource conflicts. ATMA is an overall architecture for safety-critical CPS based on a network-on-a-chip with building blocks for context agreement and adaptation. Context information is established in a globally consistent manner, providing the foundation for the temporally aligned switching of schedules in the network interfaces. A meta-scheduling algorithm computes schedule graphs and avoids state explosion with reconvergence horizons for events. For each tile, the relevant part of the schedule graph is efficiently stored using difference encodings and interpreted by the adaptation logic. The architecture was evaluated using an FPGA-based implementation and example scenarios employing adaptation for improved energy efficiency. The evaluation demonstrated the benefits of adaptation while showing the overhead and the trade-off between the degree of adaptation and the memory consumption for multi-schedule graphs.

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Global Adaptation for Energy Efficiency in Multicore Architectures

Cited by 5 publications

References 11 publications

Global Adaptation Controlled by an Interactive Consistency Protocol

Global Adaptation Controlled by an Interactive Consistency Protocol

Adaptive Scheduling for Time-Triggered Network-on-Chip-Based Multi-Core Architecture Using Genetic Algorithm

Adaptive Time-Triggered Multi-Core Architecture

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