An Overview of 4G System-Level Energy-Efficiency Performance

2016 ◽  
pp. 45-64
Author(s):  
Kazi Mohammed Saidul Huq ◽  
Shahid Mumtaz ◽  
Jonathan Rodriguez
Keyword(s):  
Energy Efficiency ◽  
System Level ◽  
Level Energy ◽  
Efficiency Performance

Assessing System-Level Energy Efficiency of mmWave-Based Wearable Networks

2016 ◽  
Vol 34 (4) ◽  
pp. 923-937 ◽  
Author(s):  
Olga Galinina ◽  
Alexander Pyattaev ◽  
Kerstin Johnsson ◽  
Andrey Turlikov ◽  
Sergey Andreev ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
System Level ◽  
Level Energy

System-level energy efficiency is the greatest barrier to development of the hydrogen economy

Energy Policy ◽  
2009 ◽  
Vol 37 (9) ◽  
pp. 3325-3335 ◽  
Author(s):  
Shannon Page ◽  
Susan Krumdieck
Keyword(s):  
Energy Efficiency ◽  
System Level ◽  
Level Energy ◽  
Hydrogen Economy

Towards Automated System-Level Energy-Efficiency Optimisation using Machine Learning

2021 ◽  
Author(s):  
Benedict Herzog ◽  
Stefan Reif ◽  
Fabian Hügel ◽  
Timo Hönig ◽  
Wolfgang Schröder-Preikschat
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Automated System ◽  
System Level ◽  
Level Energy

scholarly journals An Integrated Circuit for Radio Astronomy Correlators Supporting Large Arrays of Antennas

2016 ◽  
Vol 05 (02) ◽  
pp. 1650002 ◽  
Author(s):  
Larry R. D’Addario ◽  
Douglas Wang
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Integrated Circuit ◽  
Cross Correlation ◽  
Silicon On Insulator ◽  
System Level ◽  
Level Energy ◽  
Silicon Area ◽  
Power Efficient ◽  
The Cost

Radio telescopes that employ arrays of many antennas are in operation, and ever larger ones are being designed and proposed. Signals from the antennas are combined by cross-correlation. While the cost of most components of the telescope is proportional to the number of antennas N, the cost and power consumption of cross-correlation are proportional to [Formula: see text] and dominate at sufficiently large N. Here, we report the design of an integrated circuit (IC) that performs digital cross-correlations for arbitrarily many antennas in a power-efficient way. It uses an intrinsically low-power architecture in which the movement of data between devices is minimized. In a large system, each IC performs correlations for all pairs of antennas but for a portion of the telescope’s bandwidth (the so-called “FX” structure). In our design, the correlations are performed in an array of 4096 complex multiply-accumulate (CMAC) units. This is sufficient to perform all correlations in parallel for 64 signals (N[Formula: see text]=[Formula: see text]32 antennas with two opposite-polarization signals per antenna). When N is larger, the input data are buffered in an on-chip memory and the CMACs are reused as many times as needed to compute all correlations. The design has been synthesized and simulated so as to obtain accurate estimates of the ICs size and power consumption. It is intended for fabrication in a 32[Formula: see text]nm silicon-on-insulator process, where it will require less than 12[Formula: see text]mm2 of silicon area and achieve an energy efficiency of 1.76–3.3[Formula: see text]pJ per CMAC operation, depending on the number of antennas. Operation has been analyzed in detail up to [Formula: see text]. The system-level energy efficiency, including board-level I/O, power supplies, and controls, is expected to be 5–7[Formula: see text]pJ per CMAC operation. Existing correlators for the JVLA ([Formula: see text]) and ALMA ([Formula: see text]) telescopes achieve about 5000[Formula: see text]pJ and 1000[Formula: see text]pJ, respectively using application-specific ICs (ASICs) in older technologies. To our knowledge, the largest-N existing correlator is LEDA at [Formula: see text]; it uses GPUs built in 28[Formula: see text]nm technology and achieves about 1000[Formula: see text]pJ. Correlators being designed for the SKA telescopes ([Formula: see text] and [Formula: see text]) using FPGAs in 16[Formula: see text]nm technology are predicted to achieve about 100[Formula: see text]pJ.

scholarly journals A numerical analysis on energy-efficiency performance of temperature swing adsorption for CO 2 capture

2017 ◽  
Vol 142 ◽  
pp. 3200-3207 ◽  
Author(s):  
Junnan He ◽  
Shuai Deng ◽  
Li Zhao ◽  
Ruikai Zhao ◽  
Shuangjun Li
Keyword(s):  
Energy Efficiency ◽  
Numerical Analysis ◽  
Temperature Swing ◽  
Efficiency Performance ◽  
Temperature Swing Adsorption

Does total factor productivity affect the energy efficiency

2020 ◽  
Vol 14 (1) ◽  
pp. 108-125 ◽  
Author(s):  
Salman Haider ◽  
Javed Ahmad Bhat
Keyword(s):  
Energy Efficiency ◽  
Environmental Sustainability ◽  
Energy Efficient ◽  
Energy Intensity ◽  
Paper Industry ◽  
Labour Productivity ◽  
Efficient Technology ◽  
Content Type ◽  
Efficiency Performance ◽  
Energy Efficient Technology

Purpose Because of growing energy consumption and increasing absolute CO2 emissions, the recent calibrations about the environmental sustainability across the globe have mandated to achieve the minimal energy consumption through employing energy-efficient technology. This study aims to estimate linkage between simple measure of energy efficiency indicator that is reciprocal of energy intensity and total factor productivity (TFP) in case of Indian paper industry for 21 major states. In addition, the study incorporates the other control variables like labour productivity, capital utilization and structure of paper industry to scrutinize their likely impact on energy efficiency performance of the industry. Design/methodology/approach To derive the plausible estimates of TFP, the study applies the much celebrated Levinsohn and Petrin (2003) methodology. Using the regional level data for the period 2001-2013, the study employs instrumental variable-generalized method of moments (GMM-IV) technique to examine the nature of relationship among the variables involved in the analysis. Findings An elementary examination of energy intensity shows that not all states are equally energy intensive. States like Goa, Rajasthan, Jharkhand and Tamil Nadu are less energy intensive, whereas Uttar Pradesh, Kerala, Chhattisgarh, Assam and Punjab are most energy-intensive states on the basis of their state averages over the whole study period. The results estimated through GMM-IV show that increasing level of TFP is associated with lower level of energy per unit of output. Along this better skills and capacity utilization are also found to have positive impact on energy efficiency performance of industry. However, the potential heterogeneity within the structure of industry itself is found responsible for its higher energy intensity. Practical implications States should ensure and undertake substantial investment projects in the research and development of energy-efficient technology and that targeted allocations could be reinforced for more fruitful results. Factors aiming at improving the labour productivity should be given extra emphasis together with capital deepening and widening, needed for energy conservation and environmental sustainability. Given the dependence of structure of paper industry on the multitude of factors like regional inequality, economic growth, industrial structure and the resource endowment together with the issues of fragmented sizes, poor infrastructure and availability and affordability of raw materials etc., states should actively promote the coordination and cooperation among themselves to reap the benefits of technological advancements through technological spill overs. In addition, owing to their respective state autonomies, state governments should set their own energy saving targets by taking into account the respective potentials and opportunities for the different industries. Despite the requirement of energy-efficient innovations, however, the cons of technological advancements and the legal frameworks on the employment structure and distributional status should be taken care of before their adoption and execution. Originality/value To the best of our knowledge, this is the first study that empirically examines the linkage between energy efficiency and TFP in case of Indian paper industry. The application of improved methods like Levinsohn and Petrin (2003) to derive the TFP measure and the use of GMM-IV to account for potential econometric problems like that of endogeneity will again add to the novelty of study.

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