A Significance-Driven Programming Framework for Energy-Constrained Approximate Computing

Approximate execution is a viable technique for energy-con\-strained environments, provided that applications have the mechanisms to produce outputs of the highest possible quality within the given energy budget.
We introduce a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows users to express the relative importance of computations for the quality of the end result, as well as minimum quality requirements. The significance-aware runtime system uses an application-specific analytical energy model to identify the degree of concurrency and approximation that maximizes quality while meeting user-specified energy constraints. Evaluation on a dual-socket 8-core server shows that the proposed
framework predicts the optimal configuration with high accuracy, enabling energy-constrained executions that result in significantly higher quality compared to loop perforation, a compiler approximation technique.

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Approximate execution is a viable technique for environments with energy constraints, provided that applications are given the mechanisms to produce outputs of the highest possible quality within the available energy budget. This paper introduces a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows developers to structure the computation in different tasks, and to express the relative importance of these tasks for the quality of the end result. For non-significant tasks, the developer can also supply less costly, approximate versions. The target energy consumption for a given execution is specified when the application is launched. A significance-aware runtime system employs an application-specific analytical energy model to decide how many cores to use for the execution, the operating frequency for these cores, as well as the degree of task approximation, so as to maximize the quality of the output while meeting the user-specified energy constraints. Evaluation on a dual-socket 16-core Intel platform using 9 benchmark kernels shows that the proposed framework picks the optimal configuration with high accuracy. Also, a comparison with loop perforation (a well-known compile-time approximation technique), shows that the proposed framework results in significantly higher quality for the same energy budget.

Can we meet targets for biofuels and renewable energy in transport given the constraints imposed by policy in agriculture and energy?

The deployment of biofuels is significantly affected by policy in energy and agriculture. In the energy arena, concerns regarding the sustainability of biofuel systems and their impact on food prices led to a set of sustainability criteria in EU Directive 2009/28/EC on Renewable Energy. In addition, the 10% biofuels target by 2020 was replaced with a 10% renewable energy in transport target. This allows the share of renewable electricity used by electric vehicles to contribute to the mix in achieving the 2020 target. Furthermore, only biofuel systems that effect a 60% reduction in greenhouse gas emissions by 2020 compared with the fuel they replace are allowed to contribute to meeting the target. In the agricultural arena, cross-compliance (which is part of EU Common Agricultural Policy) dictates the allowable ratio of grassland to total agricultural land, and has a significant impact on which biofuels may be supported. This paper outlines the impact of these policy areas and their implications for the production and use of biofuels in terms of the 2020 target for 10% renewable transport energy, focusing on Ireland. The policies effectively impose constraints on many conventional energy crop biofuels and reinforce the merits of using biomethane, a gaseous biofuel. The analysis shows that Ireland can potentially satisfy 15% of renewable energy in transport by 2020 (allowing for double credit for biofuels from residues and ligno-cellulosic materials, as per Directive 2009/28/EC) through the use of indigenous biofuels: grass biomethane, waste and residue derived biofuels, electric vehicles and rapeseed biodiesel. © 2010 Elsevier Ltd. All rights reserved.

Maximum Outage Capacity in Dense Indoor Femtocell Networks with Joint Energy and Spectrum Utilization

We consider a multiple femtocell deployment in a small area which shares spectrum with the underlaid macrocell. We design a joint energy and radio spectrum scheme which aims not only for co-existence with the macrocell, but also for an energy-efficient implementation of the multi-femtocells. Particularly, aggregate energy usage on dense femtocell channels is formulated taking into account the cost of both the spectrum and energy usage. We investigate an energy-and-spectral efficient approach to balance between the two costs by varying the number of active sub-channels and their energy. The proposed scheme is addressed by deriving closed-form expressions for the interference towards the macrocell and the outage capacity. Analytically, discrete regions under which the most promising outage capacity is achieved by the same size of active sub-channels are introduced. Through a joint optimization of the sub-channels and their energy, properties can be found for the maximum outage capacity under realistic constraints. Using asymptotic and numerical analysis, it can be noticed that in a dense femtocell deployment, the optimum utilization of the energy and the spectrum to maximize the outage capacity converges towards a round-robin scheduling approach for a very small outage threshold. This is the inverse of the traditional greedy approach. © 2012 IEEE.

Selecting users in energy-efficient collaborative spectrum sensing

Cognitive radio network is defined as an intelligent wireless communication network that should be able to adaptively reconfigure its communication parameters to meet the demands of the transmission network or the user. In this context one possible way to utilize unused licensed spectrum without interfering with incumbent users is through spectrum sensing. Due to channel uncertainties, single cognitive (opportunistic) user cannot make a decision reliably and hence collaboration among multiple users is often required. Here collaboration among large number of users tends to increase power consumption and introduces large communication overheads. In this paper, the number of collaborating users is optimized in order to maximize the probability of detection for any given power budget in a cognitive radio network, while satisfying constraints on the false alarm probability. We show that for the maximum probability of detection, collaboration of only a subset of available opportunistic users is required. The robustness of our proposed spectrum sensing algorithm is also examined under flat Rayleigh fading and AWGN channel conditions.

Wireless Energy Harvesting and Spectrum Sharing in Cognitive Radio

A wireless energy harvesting protocol is proposed for a decode-and-forward relay- assisted secondary user (SU) network in a cognitive spectrum sharing paradigm. An expression for the outage probability of the relay-assisted cognitive network is derived subject to the following power constraints: 1) the maximum power that the source and the relay in the SU network can transmit from the harvested energy, 2) the peak interference power from the source and the relay in the SU network at the primary user (PU) network, and 3) the interference power of the PU network at the relay-assisted SU network. The results show that as the energy harvesting conversion efficiency improves, the relay- assisted network with the proposed wireless energy harvesting protocol can operate with outage probabilities below 20% for some practical applications.

HERQULES:System level cross-layer design exploration for efficient energy-quality trade-offs

In this paper, we present a unique cross-layer design framework that allows systematic exploration of the energy-delay-quality trade-offs at the algorithm, architecture and circuit level of design abstraction for each block of a system. In addition, taking into consideration the interactions between different sub-blocks of a system, it identifies the design solutions that can ensure the least energy at the "right amount of quality" for each sub-block/system under user quality/delay constraints. This is achieved by deriving sensitivity based design criteria, the balancing of which form the quantitative relations that can be used early in the system design process to evaluate the energy efficiency of various design options. The proposed framework when applied to the exploration of energy-quality design space of the main blocks of a digital camera and a wireless receiver, achieves 58% and 33% energy savings under 41% and 20% error increase, respectively. © 2010 ACM.

Progress at JET in integrating ITER-relevant core and edge plasmas within the constraints of an ITER-like wall

This paper reports the progress made at JET-ILW on integrating the requirements of the reference ITER baseline scenario with normalized confinement factor of 1, at a normalized pressure of 1.8 together with partially detached divertor whilst maintaining these conditions over many energy confinement times. The 2.5 MA high triangularity ELMy H-modes are studied with two different divertor configurations with D-gas injection and nitrogen seeding. The power load reduction with N seeding is reported. The relationship between an increase in energy confinement and pedestal pressure with triangularity is investigated. The operational space of both plasma configurations is studied together with the ELM energy losses and stability of the pedestal of unseeded and seeded plasmas. The achievement of stationary plasma conditions over many energy confinement times is also reported.

Cosmological Constraints from Measurements of Type Ia Supernovae Discovered During the First 1.5 Yr of the Pan-STARRS1 Survey

We present griz_P1 light curves of 146 spectroscopically confirmed Type Ia supernovae (SNe Ia; 0.03 < z < 0.65) discovered during the first 1.5 yr of the Pan-STARRS1 Medium Deep Survey. The Pan-STARRS1 natural photometric system is determined by a combination of on-site measurements of the instrument response function and observations of spectrophotometric standard stars. We find that the systematic uncertainties in the photometric system are currently 1.2% without accounting for the uncertainty in the Hubble Space Telescope Calspec definition of the AB system. A Hubble diagram is constructed with a subset of 113 out of 146 SNe Ia that pass our light curve quality cuts. The cosmological fit to 310 SNe Ia (113 PS1 SNe Ia + 222 light curves from 197 low-z SNe Ia), using only supernovae (SNe) and assuming a constant dark energy equation of state and flatness, yields w = -1.120^+0.360_-0.206(Stat)^+0.269_0.291(Sys). When combined with BAO+CMB(Planck)+H₀, the analysis yields Ω_M = 0.280^+0.013_0.012 and w = -1.166^+0.072_-0.069 including all identified systematics. The value of w is inconsistent with the cosmological constant value of -1 at the 2.3σ level. Tension endures after removing either the baryon acoustic oscillation (BAO) or the H₀ constraint, though it is strongest when including the H₀ constraint. If we include WMAP9 cosmic microwave background (CMB) constraints instead of those from Planck, we find w = -1.124^+0.083_-0.065, which diminishes the discord to <2σ. We cannot conclude whether the tension with flat ΛCDM is a feature of dark energy, new physics, or a combination of chance and systematic errors. The full Pan-STARRS1 SN sample with ∼three times as many SNe should provide more conclusive results.

Optimal day-ahead scheduling of integrated urban energy systems

An optimal day-ahead scheduling method (ODSM) for the integrated urban energy system (IUES) is introduced, which considers the reconfigurable capability of an electric distribution network. The hourly topology of a distribution network, a natural gas network, the energy centers including the combined heat and power (CHP) units, different energy conversion devices and demand responsive loads (DRLs), are optimized to minimize the day-ahead operation cost of the IUES. The hourly reconfigurable capability of the electric distribution network utilizing remotely controlled switches (RCSs) is explored and discussed. The operational constraints from the unbalanced three-phase electric distribution network, the natural gas network, and the energy centers are considered. The interactions between the electric distribution network and the natural gas network take place through conversion of energy among different energy vectors in the energy centers. An energy conversion analysis model for the energy center was developed based on the energy hub model. A hybrid optimization method based on genetic algorithm (GA) and a nonlinear interior point method (IPM) is utilized to solve the ODSM model. Numerical studies demonstrate that the proposed ODSM is able to provide the IUES with an effective and economical day-ahead scheduling scheme and reduce the operational cost of the IUES.

Benchmarking Energy Transitions: An Irish Case Study

The challenges of a low carbon energy transition have now been recognized by most nation states, each of whom have responded with differing visions, strategies and programmes, with variable veracity and effectiveness. Given the complexity of each country’s energy system (and sub-systems such as mobility, food etc), the differing sources and wealth of indigenous energy resources, the variable legacy of the fossil fuel regime and differing capacity to respond to global shifts in energy markets, it is clear that each country will respond to this challenge in very different ways.
This poses difficulties for understanding the extent to which a transition may be taking hold in any territory as simple indicators such as GHG emission data or increases in renewable energy ignore the complex contexts in which transitions take place. Drawing on the results of a study, funded by the Irish Environmental Protection Agency (Characterizing and Catalyzing Transitions) and using the wider theoretical framework of socio-technological transitions, this paper will explore the challenges, virtues and constraints of attempting to ‘benchmark’ the Republic of Ireland’s transition. This will lead to wider observations on the normative nature of benchmarking and a critical review of how we conceptualize the very idea of transition.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.