Gain scheduled control for nonlinear power management in CMPs

Donyanavard, Bryan; Rahmani, Amir M.; Mück, Tiago; Moazemmi, Kasra; Dutt, Nikil

doi:10.23919/date.2018.8342141

Cited by 14 publications

(6 citation statements)

References 11 publications

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“…In order to be able to take predicted responsive actions against nonlinear behavior of the computer systems, a well-established and lightweight adaptive control-theoretic technique called Gain Scheduling can be used. This method is used for dynamic power management in chip multiprocessors in [2].…”

Section: Adaptive Control Methodsmentioning

confidence: 99%

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Reflecting on Self-Aware Systems-on-Chip

Donyanavard

Mück

Moazzemi

et al. 2020

A Journey of Embedded and Cyber-Physical Systems

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Introduction to Self-Aware Systems-on-ChipWe are seeing an increasing number of complex cyber-physical systems (CPS) deployed for various applications, such as road-traffic control involving communicating autonomous cars and infrastructure, or smart grids controlling energy delivery down to the individual device. These distributed applications follow common design objectives, such as energy-efficiency, and require guarantees for high availability, real time or safety. In this context, autonomy is crucial: multiple system goals varying over time need to be adaptively managed and objectives holistically coordinated. By empowering future CPS with self-awareness, these systems promise to dynamically adapt, learn, and manage unforeseen changes [6].

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Section: Adaptive Control Methodsmentioning

confidence: 99%

“…The framework is open source and available online. 2 While the current version of MARS targets energy-efficient heterogeneous SoCs, we believe the MARS framework can be ported to a wider range of systems (e.g., webservers, high-performance clusters) to support self-aware resource management.…”

Section: Sensors and Actuatorsmentioning

confidence: 99%

Reflecting on Self-Aware Systems-on-Chip

Donyanavard

Mück

Moazzemi

et al. 2020

A Journey of Embedded and Cyber-Physical Systems

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“…SPECTR is a scalable and robust control architecture and a systematic design flow for hierarchical control of many-core systems. It leverages SCT techniques such as gain scheduling (Donyanavard et al, 2018) to allow autonomy for individual controllers while facilitating automatic synthesis of the high-level supervisory controller and its property verification.…”

Section: Limitations Of Current Approaches and Recent Trendsmentioning

confidence: 99%

On-Chip Dynamic Resource Management

Miele

Kanduri²,

Moazzemi³

et al. 2019

FNT in Electronic Design Automation

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The need for dynamic resource management has shadowed the exponential growth of on-chip transistor capacity, and the challenge is accentuated by the heterogeneity of resources, and the bewildering variety of constraints and requirements of applications, platforms and users. The field has started with a few research papers in the early 1990s but has grown today to over hundred yearly publications, leading to an accumulated body of literature presumable far above 1000 papers. We focus on the dynamic (run-time) management of onchip resources and mostly ignore design-time techniques and off-chip resources of larger electronic systems. Moreover, we do not attempt a complete review of all published work on the topic. Rather, this survey provides a structured review and discussion of the state of the art and is divided along the primary objectives of resource management techniques: performance, power, reliability and quality of service, each

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“…As a result, a system may display non-ideal behavior according to the model used to design the controller. The controller would make potentially poor decisions due to an inaccurate model [14]. The challenges outlined so far assume that the system dynamics being modeled can be estimated with a simple linear equation, which is the case for the frequency→HB controller.…”

Section: Motivation 31 Challenges Of Model-dependencementioning

confidence: 99%

Sosa

Donyanavard

Mück

Rahmani

et al. 2019

Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture

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Resource management strategies for many-core systems dictate the sharing of resources among applications such as power, processing cores, and memory bandwidth in order to achieve system goals. System goals require consideration of both system constraints (e.g., power envelope) and user demands (e.g., response time, energyefficiency). Existing approaches use heuristics, control theory, and machine learning for resource management. They all depend on static system models, requiring a priori knowledge of system dynamics, and are therefore too rigid to adapt to emerging workloads or changing system dynamics. We present SOSA, a cross-layer hardware/software hierarchical resource manager. Low-level controllers optimize knob configurations to meet potentially conflicting objectives (e.g., maximize throughput and minimize energy). SOSA accomplishes this for many-core systems and unpredictable dynamic workloads by using rule-based reinforcement learning to build subsystem models from scratch at runtime. SOSA employs a high-level supervisor to respond to changing system goals due to operating condition, e.g., switch from maximizing performance to minimizing power due to a thermal event. SOSA's supervisor translates the system goal into lowlevel objectives (e.g., core instructions-per-second (IPS)) in order to control subsystems by coordinating numerous knobs (e.g., core operating frequency, task distribution) towards achieving the goal. The software supervisor allows for flexibility, while the hardware learners allow quick and efficient optimization. We evaluate a simulation-based implementation of SOSA and demonstrate SOSA's ability to manage multiple interacting resources in the presence of conflicting objectives, its efficiency in configuring

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Gain scheduled control for nonlinear power management in CMPs

Cited by 14 publications

References 11 publications

Reflecting on Self-Aware Systems-on-Chip

Reflecting on Self-Aware Systems-on-Chip

On-Chip Dynamic Resource Management

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