Prospects of Near-Threshold Voltage Design for Green Computing

Khare, Surhud; Jain, Shailendra

doi:10.1109/vlsid.2013.174

Cited by 14 publications

(5 citation statements)

References 3 publications

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“…Early studies [1], [2] have shown that near-threshold voltage operation provides an optimal design point in respect to energy and performance efficiency. Early NT voltage processor prototypes [8], [9] have been recently demonstrated validating the theoretical premises, while several studies show the high efficiency of NTC for cloud- [10] and server-based workloads [11].…”

Section: Ntc Manycore Architecturesmentioning

confidence: 86%

Workload- and process-variation aware voltage/frequency tuning for energy efficient performance sustainability of NTC manycores

et al. 2019

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Section: Ntc Manycore Architecturesmentioning

confidence: 86%

Workload- and process-variation aware voltage/frequency tuning for energy efficient performance sustainability of NTC manycores

et al. 2019

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“…We also believe that the DPM technology helps the system automatically change its power rates depending on the current situation [102,103]. Moreover, the workload requirements can also be estimated, which allows the adjustment of future system behavior to execute adequate action under these requirements [107].…”

Section: Dynamic Power Management (Dpm)mentioning

confidence: 99%

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

et al. 2022

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Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

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“…We instead neglect failures due to particle strikes, since they are rare events (few can be expected in a billion hours of operation, with a weak dependency on the operating voltage [48]). SRAM banks, hosting instructions and data, are in fact the least reliable elements of digital architectures when operating at ultra-low voltage levels [24], while combinational circuits (such as Arithmetic Logic Units, ALUs) and Flip-Flop-based registers have higher resiliencies.…”

Section: :3mentioning

confidence: 99%

“…Logic components can instead be er tolerate lower voltage supplies, especially when operating at low frequencies of a few MHz, which is usual for ultra-low power systems. Indeed, a dedicated NTC architecture, having separate voltage domains for memories and logic, has been introduced by the authors of [24]. However, this strategy incurs a large overhead to generate and distribute di erent supply levels and for the voltage converters, required for signals crossing the di erent voltage islands [31].…”

Section: Inexact and Near-threshold Computingmentioning

confidence: 99%

An Inexact Ultra-low Power Bio-signal Processing Architecture With Lightweight Error Recovery

Basu

Duch

Braojos

et al. 2017

ACM Trans. Embed. Comput. Syst.

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e energy e ciency of digital architectures is tightly linked to the voltage level (Vdd) at which they operate. Aggressive voltage scaling is therefore mandatory when ultra-low power processing is required. Nonetheless, the lowest admissible Vdd is o en bounded by reliability concerns, especially since static and dynamic non-idealities are exacerbated in the near-threshold region, imposing costly guard-bands to guarantee correctness under worst-case conditions. A striking alternative, explored in this paper, waives the requirement for unconditional correctness, undergoing more relaxed constraints. First, a er a run-time failure, processing correctly resumes at a later point in time. Second, failures induce a limited ality-of-Service (QoS) degradation. We focus our investigation on the practical scenario of embedded bio-signal analysis, a domain in which energy e ciency is key, while applications are inherently error-tolerant to a certain degree. Targeting a domain-speci c multi-core platform, we present a study of the impact of inexactness on application-visible errors. en, we introduce a novel methodology to manage them, which requires minimal hardware resources and a negligible energy overhead. Experimental evidence show that, by tolerating 900 errors/hour, the resulting inexact platform can achieve an e ciency increase of up to 24%, with a QoS degradation of less than 3%. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro t or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permi ed. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior speci c permission and/or a fee. Request permissions from permissions@acm.org. INTRODUCTIONe emergence of embedded devices, able to continuously acquire and wirelessly transmit the sensed data, is fostering a revolution across the IT landscape [3], opening novel and exciting opportunities in many elds, ranging from environmental protection [30] to domotics [16].Among them, healthcare applications are of particular interest, especially the ones related to monitoring chronic cardiovascular disorders [1]. In this scenario, sensor appliances (named Wireless Body Sensor Nodes, WBSNs) enable the long-term acquisition of bio-signals, outside of a hospital environment and with minimal medical supervision [18].E ciency is key for WBSNs, as the saved energy translates both in smaller form factors (by requiring smaller ba eries) and longer autonomies. Herein, we focus our investigation on the energy optimization of the Digital Signal Processing (DSP) applications executing on WBSNs. Such routines analyze acquisitions, deriving compact feature sets, which are then transmi ed on the wireless link [7]. ey must be supported within a tight energy envelope, because...

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Prospects of Near-Threshold Voltage Design for Green Computing

Cited by 14 publications

References 3 publications

Workload- and process-variation aware voltage/frequency tuning for energy efficient performance sustainability of NTC manycores

Workload- and process-variation aware voltage/frequency tuning for energy efficient performance sustainability of NTC manycores

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

An Inexact Ultra-low Power Bio-signal Processing Architecture With Lightweight Error Recovery

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