P<sup>4</sup>: Phase-based power/performance prediction of heterogeneous systems via neural networks

Kim, Yeseong; Mercati, Pietro; More, Ankit; Shriver, Emily; Rosing, Tajana

doi:10.1109/iccad.2017.8203843

Cited by 26 publications

(12 citation statements)

References 10 publications

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“…In this step, PMCs count sponding to the predicted phase and an interval vector is made. There is a s Kim et al [13] classified phases using PMC events and trained a neural network to capture per-phase patterns. However, they used only a single power model for different phases rather than different power models for different phases.…”

Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

“…However, per-phase patterns of their work are the program patterns because basic blocks are generated by a compiler. In order to use hardware operation patterns rather than patterns of BBV, Kim et al [13] and Zheng et al [14] used PMC events for phase classification and devised PMC-based phase classification for power estimation. Kim et al [13] used phase classification for accurate cross-platform power/performance estimation.…”

Section: Phase Classification and Its Applicationmentioning

confidence: 99%

“…In order to use hardware operation patterns rather than patterns of BBV, Kim et al [13] and Zheng et al [14] used PMC events for phase classification and devised PMC-based phase classification for power estimation. Kim et al [13] used phase classification for accurate cross-platform power/performance estimation. They created the cumulative distribution function (CDF) graphs of each event for different phases and different platforms.…”

Section: Phase Classification and Its Applicationmentioning

confidence: 99%

“…Therefore, by selecting an optimal even sponding to a specific phase or workload at run-time, it will be possible to es consumption more accurately. Kim et al [13] classified phases using PMC events and trained a neur capture per-phase patterns. However, they used only a single power mode phases rather than different power models for different phases.…”

Section: Ldst_inst Rate =mentioning

confidence: 99%

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Phase-Based Accurate Power Modeling for Mobile Application Processors

et al. 2021

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Modern mobile application processors are required to execute heavier workloads while the battery capacity is rarely increased. This trend leads to the need for a power model that can analyze the power consumed by CPU and GPU at run-time, which are the key components of the application processor in terms of power savings. We propose novel CPU and GPU power models based on the phases using performance monitoring counters for smartphones. Our phase-based power models employ combined per-phase power modeling methods to achieve more accurate power consumption estimations, unlike existing power models. The proposed CPU power model shows estimation errors of 2.51% for ARM Cortex A-53 and 1.97% for Samsung M1 on average, and the proposed GPU power model shows an average error of 8.92% for the Mali-T880. In addition, we integrate proposed CPU and GPU models with the latest display power model into a holistic power model. Our holistic power model can estimate the smartphone′s total power consumption with an error of 6.36% on average while running nine 3D game benchmarks, improving the error rate by about 56% compared with the latest prior model.

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Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

Section: Phase Classification and Its Applicationmentioning

confidence: 99%

Section: Phase Classification and Its Applicationmentioning

confidence: 99%

Section: Ldst_inst Rate =mentioning

confidence: 99%

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Phase-Based Accurate Power Modeling for Mobile Application Processors

et al. 2021

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“…Yeseong et al [32] propose a similar approach with a complete split between the resource consumption models of applications and the hardware power consumption models. At the cost of precision that reaches 7%, the proposed models are able to be run on different hardware with a minimal relearning cost.…”

Section: Formula-learned Models Using Machine Learningmentioning

confidence: 99%

Effectiveness of Neural Networks for Power Modeling for Cloud and HPC

Costa

Pierson

Fontoura‐Cupertino

2020

ACM Trans. Model. Perform. Eval. Comput. Syst.

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Power consumption of servers and applications are of utmost importance as computers are becoming ubiquitous, from smart phones to IoT and full-fledged computers. To optimize their power consumption, knowledge is necessary during execution at different levels: for the Operating System to take decisions of scheduling, for users to choose between different applications. Several models exist to evaluate the power consumption of computers without relying on actual wattmeters: Indeed, these hardware are costly but also usually have limits on their pooling frequency (usually a one-second frequency is observed) except for dedicated professional hardware. The models link applications behavior with their power consumption, but up to now there is a 5% wall: Most models cannot reduce their error under this threshold and are usually linked to a particular hardware configuration. This article demonstrates how to break the 5% wall of power models. It shows that by using neural networks it is possible to create models with 1% to 2% error. It also quantifies the reachable precision obtainable with other classical methods such as analytical models.

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NetHD: Neurally Inspired Integration of Communication and Learning in Hyperspace

Poduval,

Ni,

Zou

et al. 2024

Advanced Intelligent Systems

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The 6G network, the next‐generation communication system, is envisaged to provide unprecedented experience through hyperconnectivity involving everything. The communication should hold artificial intelligence‐centric network infrastructures as interconnecting a swarm of machines. However, existing network systems use orthogonal modulation and costly error correction code; they are very sensitive to noise and rely on many processing layers. These schemes impose significant overhead on low‐power internet of things devices connected to noisy networks. Herein, a hyperdimensional network‐based system, called , is proposed, which enables robust and efficient data communication/learning. exploits a redundant and holographic representation of hyperdimensional computing (HDC) to design highly robust data modulation, enabling two functionalities on transmitted data: 1) an iterative decoding method that translates the vector back to the original data without error correction mechanisms, or 2) a native hyperdimensional learning technique on transmitted data with no need for costly data decoding. A hardware accelerator that supports both data decoding and hyperdimensional learning using a unified accelerator is also developed. The evaluation shows that provides a bit error rate comparable to that of state‐of‐the‐art modulation schemes while achieving 9.4 faster and 27.8 higher energy efficiency compared to state‐of‐the‐art deep learning systems.

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P⁴: Phase-based power/performance prediction of heterogeneous systems via neural networks

Cited by 26 publications

References 10 publications

Phase-Based Accurate Power Modeling for Mobile Application Processors

Phase-Based Accurate Power Modeling for Mobile Application Processors

Effectiveness of Neural Networks for Power Modeling for Cloud and HPC

NetHD: Neurally Inspired Integration of Communication and Learning in Hyperspace

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P4: Phase-based power/performance prediction of heterogeneous systems via neural networks

Cited by 26 publications

References 10 publications

Phase-Based Accurate Power Modeling for Mobile Application Processors

Phase-Based Accurate Power Modeling for Mobile Application Processors

Effectiveness of Neural Networks for Power Modeling for Cloud and HPC

NetHD: Neurally Inspired Integration of Communication and Learning in Hyperspace

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Product

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About

P⁴: Phase-based power/performance prediction of heterogeneous systems via neural networks