Voltage over-scaling:A cross-layer design perspective for energy efficient systems

Today's multi-media electronic era is driven by the increasing demand for small multifunctional devices able to support diverse services. Unfortunately, the high levels of transistor integration and performance required by such devices lead to an unprecedented increase of on-chip power that significantly limits the battery lifetime and even poses reliability concerns. Several techniques have been developed to address the power increase, but voltage over-scaling (VOS) is considered to be one of the most effective ones due to the quadratic dependence of voltage on dynamic power consumption. However, VOS may not always be applicable since it increases the delay in all paths of a system and may limit high performance required by today's complex applications. In addition, application of VOS is further complicated since it increases the variations in transistor characteristics imposed by their tiny size which can lead to large delay and leakage variations, making it difficult to meet delay and power budgets. This paper presents a review of various cross-layer design options that can provide solutions for dynamic voltage over-scaling and can potentially assist in meeting the strict power budgets and yield/quality requirements of future systems. © 2011 IEEE.

VEDA:Variation-aware energy-efficient discrete Wavelet Transform architecture

In this paper, we present a unified approach to an energy-efficient variation-tolerant design of Discrete Wavelet Transform (DWT) in the context of image processing applications. It is to be noted that it is not necessary to produce exactly correct numerical outputs in most image processing applications. We exploit this important feature and propose a design methodology for DWT which shows energy quality tradeoffs at each level of design hierarchy starting from the algorithm level down to the architecture and circuit levels by taking advantage of the limited perceptual ability of the Human Visual System. A unique feature of this design methodology is that it guarantees robustness under process variability and facilitates aggressive voltage over-scaling. Simulation results show significant energy savings (74% - 83%) with minor degradations in output image quality and avert catastrophic failures under process variations compared to a conventional design. © 2010 IEEE.

ZIL: An Energy-Efficient Indoor Localization System Using ZigBee Radio to Detect WiFi Fingerprints

In existing WiFi-based localization methods, smart mobile devices consume quite a lot of power as WiFi interfaces need to be used for frequent AP scanning during the localization process. In this work, we design an energy-efficient indoor localization system called ZigBee assisted indoor localization (ZIL) based on WiFi fingerprints via ZigBee interference signatures. ZIL uses ZigBee interfaces to collect mixed WiFi signals, which include non-periodic WiFi data and periodic beacon signals. However, WiFi APs cannot be identified from these WiFi signals by ZigBee interfaces directly. To address this issue, we propose a method for detecting WiFi APs to form WiFi fingerprints from the signals collected by ZigBee interfaces. We propose a novel fingerprint matching algorithm to align a pair of fingerprints effectively. To improve the localization accuracy, we design the K-nearest neighbor (KNN) method with three different weighted distances and find that the KNN algorithm with the Manhattan distance performs best. Experiments show that ZIL can achieve the localization accuracy of 87%, which is competitive compared to state-of-the-art WiFi fingerprint-based approaches, and save energy by 68% on average compared to the approach based on WiFi interface.

A quality-scalable and energy-efficient approach for spectral analysis of heart rate variability

Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design- and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case. 

Software-Managed Energy-Efficient Hybrid DRAM/NVM Main Memory

This paper evaluates the viability of user-level software management of a hybrid DRAM/NVM main memory system. We propose an operating system (OS) and programming interface to place data from within the user application. We present a profiling tool to help programmers decide on the placement of application data in hybrid memory systems. Cycle-accurate simulation of modified applications confirms that our approach is more energy-efficient than state-of-the- art hardware or OS approaches at equivalent performance. Moreover, our results are validated on several candidate NVM technologies and a wide set of 14 benchmarks.
The key observation behind this work is that, for the work- loads we evaluated, application objects are too short-lived to motivate migration. Utilizing this property significantly reduces the hardware complexity of hybrid memory systems.

A Programming Model and Runtime System for Significance-Aware Energy-Efficient Computing.

We introduce a task-based programming model and runtime system that exploit the observation that not all parts of a program are equally significant for the accuracy of the end-result, in order to trade off the quality of program outputs for increased energy-efficiency. This is done in a structured and flexible way, allowing for easy exploitation of different points in the quality/energy space, without adversely affecting application performance. The runtime system can apply a number of different policies to decide whether it will execute less-significant tasks accurately or approximately.

The experimental evaluation indicates that our system can achieve an energy reduction of up to 83% compared with a fully accurate execution and up to 35% compared with an approximate version employing loop perforation. At the same time, our approach always results in graceful quality degradation.

Energy-Efficient In-Memory Data Stores on Hybrid Memory Hierarchies

Increasingly large amounts of data are stored in main memory of data center servers. However, DRAM-based memory is an important consumer of energy and is unlikely to scale in the future. Various byte-addressable non-volatile memory (NVM) technologies promise high density and near-zero static energy, however they suffer from increased latency and increased dynamic energy consumption.

This paper proposes to leverage a hybrid memory architecture, consisting of both DRAM and NVM, by novel, application-level data management policies that decide to place data on DRAM vs. NVM. We analyze modern column-oriented and key-value data stores and demonstrate the feasibility of application-level data management. Cycle-accurate simulation confirms that our methodology reduces the energy with least performance degradation as compared to the current state-of-the-art hardware or OS approaches. Moreover, we utilize our techniques to apportion DRAM and NVM memory sizes for these workloads.

Effective indoor air quality for energy-efficient homes: a comparison of UK rating systems

The use of sustainable assessment methods in the UK is on the rise, anticipating the future regulatory trajectory towards zero carbon by 2016. The indisputable influence of sustainable rating tools on UK building regulations conveys the importance of evaluating their effectiveness in achieving true sustainable design, without adversely effecting human health and wellbeing. This paper reviews indoor air-quality (IAQ) issues addressed by UK sustainable assessment tools, and the potential trade-offs between building energy conservation and IAQ. The barriers to effective adoption of IAQ strategies are investigated, including recommendations, suggestions, and future research needs. The review identified a fundamental lack of IAQ criteria in sustainable assessment tools aimed at the residential sector. The consideration of occupants’ health and well-being should be paramount in any assessment scheme, and should not be overshadowed or obscured by the drive towards energy efficiency. A balance is essential.

An investigation of IAQ, thermal comfort and Sick Building Syndrome symptoms in UK energy efficient homes

Purpose
– Concern of the deterioration of indoor environmental quality as a result of energy efficient building design strategies is growing. Apprehensions of the effect of airtight, super insulated envelopes, the reduction of infiltration, and the reliance on mechanical systems to provide adequate ventilation (air supply) is promoting emerging new research in this field. The purpose of this paper is to present the results of an indoor air quality (IAQ) and thermal comfort investigation in UK energy efficient homes, through a case study investigation.

Design/methodology/approach
– The case study dwellings consisted of a row of six new-build homes which utilize mechanical ventilation with heat recovery (MVHR) systems, are built to an average airtightness of 2m3/m2/hr at 50 Pascal’s, and constructed without a central heating system. Physical IAQ measurements and occupant interviews were conducted during the summer and winter months over a 24-hour period, to gain information on occupant activities, perception of the interior environment, building-related health and building use.

Findings
– The results suggest inadequate IAQ and perceived thermal comfort, insufficient use of purge ventilation, presence of fungal growth, significant variances in heating patterns, occurrence of sick building syndrome symptoms and issues with the MVHR system.

Practical implications
– The findings will provide relevant data on the applicability of airtight, mechanically ventilated homes in a UK climate, with particular reference to IAQ.

Originality/value
– IAQ data of this nature is essentially lacking, particularly in the UK context. The findings will aid the development of effective sustainable design strategies that are appropriate to localized climatic conditions and sensitive to the health of building occupants.

An HMM-based spectrum occupancy predictor for energy efficient cognitive radio

Spectrum sensing is the cornerstone of cognitive radio technology and refers to the process of obtaining awareness of the radio spectrum usage in order to detect the presence of other users. Spectrum sensing algorithms consume considerable energy and time. Prediction methods for inferring the channel occupancy of future time instants have been proposed as a means of improving performance in terms of energy and time consumption. This paper studies the performance of a hidden Markov model (HMM) spectrum occupancy predictor as well as the improvement in sensing energy and time consumption based on real occupancy data obtained in the 2.4GHz ISM band. Experimental results show that the HMM-based occupancy predictor outperforms a kth order Markov and a 1-nearest neighbour (1NN) predictor. Our study also suggests that by employing such a predictive scheme in spectrum sensing, an improvement of up to 66% can be achieved in the required sensing energy and time.

Development of sustainable, innovative and energy-efficient concrete, based on the integration of all-waste materials: SUS-CON panels for building applications

The building sector requires the worldwide production of 4 billion tonnes of cement annually, consuming more than 40% of global energy and accounting for about 8% of the total CO2 emissions. The SUS-CON project aimed at integrating waste materials in the production cycle of concrete, for both ready-mixed and pre-cast applications, resulting in an innovative light-weight, ecocompatible and cost-effective construction material, made by all-waste materials and characterized by enhanced thermal insulation performance and low embodied energy and CO2. Alkali activated “cementless” binders, which have recently emerged as eco-friendly construction materials, were used in conjunction with lightweight recycled aggregates to produce sustainable concrete for a range of applications. This paper presents some results from the development of a concrete made with a geopolymeric binder (alkali activated fly ash) and aggregate from recycled mixed plastic. Mix optimisation was achieved through an extensive investigation on production parameters for binder and aggregate. The mix recipe was developed for achieving the required fresh and hardened properties. The optimised mix gave compressive strength of about 7 MPa, flexural strength of about 1.3 MPa and a thermal conductivity of 0.34 W/mK. Fresh and hardened properties were deemed suitable for the industrial production of precast products. Precast panels were designed and produced for the construction of demonstration buildings. Mock-ups of about 2.5 x 2.5 x 2.5 m were built at a demo park in Spain both with SUS-CON and Portland cement concrete, monitoring internal and external temperatures. Field results indicate that the SUS-CON mock-ups have better insulation. During the warmest period of the day, the measured temperature in the SUS-CON mock-ups was lower.