Machine learning for power, energy, and thermal management on multicore processors: A survey
Due to the high integration density and roadblock of voltage scaling, modern multicore
processors experience higher power densities than previous technology scaling nodes …
processors experience higher power densities than previous technology scaling nodes …
Distributed reinforcement learning for power limited many-core system performance optimization
As power density emerges as the main constraint for many-core systems, controlling power
consumption under the Thermal Design Power (TDP) while maximizing the performance …
consumption under the Thermal Design Power (TDP) while maximizing the performance …
Toward smart embedded systems: A self-aware system-on-chip (soc) perspective
Embedded systems must address a multitude of potentially conflicting design constraints
such as resiliency, energy, heat, cost, performance, security, etc., all in the face of highly …
such as resiliency, energy, heat, cost, performance, security, etc., all in the face of highly …
Using predictions in online optimization: Looking forward with an eye on the past
We consider online convex optimization (OCO) problems with switching costs and noisy
predictions. While the design of online algorithms for OCO problems has received …
predictions. While the design of online algorithms for OCO problems has received …
Thermal and energy management of high-performance multicores: Distributed and self-calibrating model-predictive controller
As result of technology scaling, single-chip multicore power density increases and its spatial
and temporal workload variation leads to temperature hot-spots, which may cause …
and temporal workload variation leads to temperature hot-spots, which may cause …
Spectr: Formal supervisory control and coordination for many-core systems resource management
Resource management strategies for many-core systems need to enable sharing of
resources such as power, processing cores, and memory bandwidth while coordinating the …
resources such as power, processing cores, and memory bandwidth while coordinating the …
Using multiple input, multiple output formal control to maximize resource efficiency in architectures
As processors seek more resource efficiency, they increasingly need to target multiple goals
at the same time, such as a level of performance, power consumption, and average …
at the same time, such as a level of performance, power consumption, and average …
An effective gray-box identification procedure for multicore thermal modeling
Aggressive thermal management is a critical feature for high-end computing platforms, as
worst-case thermal budgeting is becoming unaffordable. Reactive thermal management …
worst-case thermal budgeting is becoming unaffordable. Reactive thermal management …
Improving smartphone user experience by balancing performance and energy with probabilistic QoS guarantee
User satisfaction is pivotal to the success of a mobile application. A recent study has shown
that 49% of users would abandon a web-based application if it failed to load within 10 …
that 49% of users would abandon a web-based application if it failed to load within 10 …
HESSLE-FREE: He terogeneou s S ystems Le veraging F uzzy Control for R untim e Resourc e Management
As computing platforms increasingly embrace heterogeneity, runtime resource managers
need to efficiently, dynamically, and robustly manage shared resources (eg, cores, power …
need to efficiently, dynamically, and robustly manage shared resources (eg, cores, power …