Proceedings of the 2013 workshop on Energy efficient high performance parallel and distributed computing

Forecasts show that traffic demand will continue to grow exponentially over the foreseeable future and mobile operators will have to upgrade and densify their networks to support this traffic demand. However, due to the spatiotemporal variation of traffic, some base stations (BSs) may remain idle during low traffic conditions. In this paper, we use tools from stochastic geometry to analyze the capacity and energy efficiency (EE) performance of a dense heterogeneous network (HetNet). We derive a multi-user connectivity model based on the LTE standard to determine realistic BS density configurations. We implement conventional sleep mode and investigate the effect of biasing on the capacity and EE performance of the HetNet. Our results show that the prevailing user density influences the HetNet performance, and that conventional sleep mode significantly enhances the EE performance. Appropriate biasing of the HetNet results in further EE performance enhancement.

Energy efficient computing is one of the primary requirements of ubiquitous battery-operated edge devices including mobile phones, wearables, active implantable medical devices, unmanned aerial, autonomous, and electric vehicles, and hardware modules used in smart IoT infrastructure and cyber-physical systems. One of the primary sources of loss in energy efficiency is the leakage current of logic and memory circuits. In addition, the emergence of on-device intelligence requires a greater number of computations, increased storage per device, and an increased number of operations per watt. Operating circuits at ultra-low voltages including near- and sub-threshold is an effective means to improve the energy efficiency. However, circuit techniques, algorithms, and power management methodologies are required that are tailored to ultra-low voltage operation. Moreover, novel techniques and architectures are needed to improve the overall energy efficiency of circuits that implement neural network models. Techniques to reduce and conserve the leakage current of an IC are, therefore, essential to further minimize the consumed power of a circuit. In this research, novel circuits and algorithms are developed that allow for robust and energy efficient ultra-low voltage operation. Completed research tasks include the development of dynamic differential signaling based logic (DDSL) families, interface circuits that allow for signal propagation between ultra-low voltage and nominal voltage power domains, supply and threshold voltage optimization, and clock-power co-design for sub-threshold operation. The developed DDSL families operate between 400 mV and 450 mV and reduce the total power consumption to 0.96x that of CMOS logic circuits, while improving the performance and noise margin by, respectively, 1.4x and 2.5x. The developed interface circuits include a single ended level shifter and a bidirectional input/output (I/O) circuit, which efficiently convert an input voltage of 450 mV to an output voltage of 3.3 V while consuming only 4.87 mW. The developed clock-power co-design methodology provides a robust power supply voltage of 250 mV to the clock distribution network while assuring resilience to as much as 10% noise on the power network. A novel power management technique defined as 'leakage reuse' is developed to recycle the leakage current from idle circuits or cores and deliver the recycled energy to active circuit blocks or cores. The circuit techniques, scheduling algorithms, and the determination of the energy break-even point are completed for the leakage reuse technique. The power delivery to sub-Vt circuits through leakage reuse reduces the total power consumption to 0.41x that of a conventional power delivery system when generating a supply voltage of 380 mV from the recycled leakage current of circuits operating at 1.2 V. Two scheduling algorithms are developed that allow for the simultaneous execution of power gating and leakage reuse and reduce the total power consumption by 50.2% as compared to a baseline topology that includes neither leakage reuse or power gating. In addition, techniques and methodologies are developed to recycle leakage energy from idle on-chip SRAM memories. Moreover, a novel multi-voltage domain heterogeneous accelerator architecture is developed for energy efficient execution of neural network models. A near-memory computing architecture is developed for the heterogeneous accelerator that leverages the leakage reuse technique applied to an SRAM memory array, where the leakage current of idle memory banks within each processing element is utilized to deliver current to the adjacently placed multiply-and-accumulate (MAC) units. The proposed heterogeneous architecture with leakage reuse results in an energy efficiency of 3.27 tera-operations per second per watt (TOPS/W) as compared to a conventional monolithic and single voltage domain architecture that exhibits an energy efficiency of 0.0458 TOPS/W.

Thermoelectric (TE) generation is becoming a valuable and promising research direction. Many researchers have carried out system analysis and performance optimization of thermoelectric technologies based on the generalized thermoelectric energy balance equations. However, it is assumed that TE legs have no heat exchange with the ambient except at the junctions of the hot and cold ends where heat flows in and out. Based on basic thermoelectric effects and fundamental theories of heat transfer, a detailed derivation of the revised generalized thermoelectric energy equations considering convective heat transfer between TE legs and the ambient has been carried out. Irreversible heat transfer processes have been analyzed by employing energy analysis based on the first law of thermodynamics and exergy analysis based on the second law of thermodynamics. The results show that convective heat transfer leads to a decrease in both energy and exergy efficiencies: the rate and magnitude of the decrease in exergy efficiency are greater than those of the decrease in energy efficiency. The exergy efficiency is relatively high despite the low energy efficiency in operation, revealing the features and advantages of thermoelectric generators (TEGs) in low‐grade energy utilization. For TEG efficient operation, the load resistance value should match the system's internal resistance, or at least be greater than that, to avoid a sharp drop in power output and efficiencies. In an attempt at theoretical analysis, the concept of entransy was first introduced into thermoelectric analysis, yielding two concise relational equations which reflect the intrinsic link between Carnot cycle efficiency, energy efficiency, exergy efficiency, and entransy flow transfer efficiency. The entransy analysis based on the index of entransy flow transfer efficiency, together with energy analysis and exergy analysis, may be a novel and valuable guideline for the operation and optimization of TEGs, which needs to be further investigated.

The sixth-generation (6G) of communication systems mandates high energy efficiency and seamless integration of joint communication and computation functionalities, supported by native machine learning (ML) capabilities. Furthermore, robustness to channel and computing uncertainties remains indispensable for the practical realization of future wireless networks. To this end, we propose a robust, ML-based framework that maximizes a joint communication and computation network-wide energy efficiency objective in cell-free wireless networks. We focus on a coordinated processing architecture, where multiple single-antenna distributed access points (APs) collaboratively serve the network users. The system performance then becomes a strong function of the joint communication and computation resource allocation strategy, which in turn depends on the uncertainties in channel state information and computing task requirements. We, therefore, propose solving the problem using a data-driven joint uncertainty injection method, whereby a fine-tuned deep neural network (DNN) learns an energy-efficient power and computation allocation strategy, with the total power intelligently split between the transmission and computation. Simulation results show that the proposed approach outperforms uncertainty-agnostic and non-ML baselines, demonstrating its potential for resilient and energy-efficient operation in future artificial intelligence (AI)native, cell-free networks.

Energy efficiency (EE) performance of millimeter-wave (mmWave) large-array systems attracts a lot of attention. And insertion loss is an inherent characteristic of hybrid precoding systems, which greatly reduces the system performance. EE analysis considering the insertion loss based on the dynamic array architecture is still an open issue. This paper researches the ergodic EE of mmWave hybrid pre-coding system with the insertion loss, based on the adaptive overlapped subarray (OSA) architecture. The ergodic EE is a more valuable indicator than the instantaneous EE for the system under dynamic architecture. However, it is difficult to directly calculate the precise ergodic EE due to the intractable relationship between EE and the channel matrix. Instead, we simplify the precise ergodic EE to get a lower bound, and analyze the ergodic EE performance based upon the lower bound. By this way, we analyze the ergodic EE performance of two typical hybrid pre-coding schemes taking the dynamic architectures. Simulation results verify the effectiveness of analyses.

This paper includes the survey of need and issues/challenges faced for energy efficiency in 5G network and D2D technique to improve energy efficiency without compromising the user experience. Improving the energy efficiency is important part while designing a network. Energy efficient communication is required for energy constrained networks such as ad-hoc networks in which battery powered wireless system need to reduce energy usage. Cellular systems need to be energy efficient, particularly as the battery constraints has been made significant problem in mobile communication. Also, significant part of the energy is absorbed by the base station, which produces a large electricity bill. This has urged energy optimization for the use of mobile phones, the achievement of energy efficiency is of great economic value from both, the operator’s and the consumer’s viewpoint. Keywords: Base station (BS), energy efficiency (EE), device-to-device technology (D2D) Cite this Article Anika Chaudhary, Himanshi, Pooja Rani. Energy efficiency issues and D2D technique in 5G network. Journal of Telecommunication, Switching Systems and Networks . 2020; 7(2): 1–6p.

Energy efficient communications in cognitive radio (CR) networks has been of great interest. In order to address the power consumption problem and to maximise the energy efficiency (EE) in MIMO-OFDM underlay CR systems, power allocation (PA) and antenna selection (AS) algorithms are proposed. Interference alignment, which helps in mitigating the interference at primary and secondary receivers, is considered for setting-up the underlay CR scenario. The AS transmit is employed for the secondary users (SUs) on a per-subcarrier basis to improve their QoS. Further, to improve the QoS of both primary user (PU) and SUs, the AS is followed by energy efficient PA. Due to the fractional nature of EE, the EE maximisation problem is transformed into an equivalent parametric optimisation problem based on Dinkelbach method. Simulation results confirm that the proposed EE aware algorithms can improve the EE performance of both PU and SUs than the SE aware algorithms.

This paper studies the energy efficiency (EE) of the cloud radio access network (C-RAN), consisting of multiple remote radio heads (RRHs) equipped with electronically steerable parasitic array radiator (ESPAR) antennas, which provide multiple antenna functionality with a single radio frequency (RF) chain. An EE optimization problem is formulated to obtain the configuration of ESPAR and the closed-form expressions of the voltage feeding and the loadings are derived for signal transmission at each RRH. Specifically, we obtain the closed-form expressions for precoder and power allocation that are applicable not only for the ESPAR based system but also for standard MIMO antenna (SMA) system with multiple RF chains. It is shown that EA system can be configured with less complexity compared with SMA system in block fading channel. Furthermore, symbol error rate (SER) and EE performances are compared for EA and SMA based systems. It is shown that the system with EA provides better EE performance while providing similar SER performance. From our results, it is proved that EA system can provide better performance to satisfy the requirement of 5G wireless communication networks.

Energy use and energy efficiency development in the German and Colombian textile industries

Global energy efficiency transition tendencies: Development phenomenon or not?

High Energy efficiency and high performance are the key regiments for Internet of Things (IoT) edge devices. Exploiting cluster of multiple programmable processors has recently emerged as a suitable solution to address this challenge. However, one of the main power bottlenecks for multi-core architectures is the instruction cache memory. We propose a two-level structure based on Standard Cell Memories (SCMs) which combines a private instruction cache (L1) per-core and a low-latency (only one cycle latency) shared instruction cache (L1,5). We present a detailed comparison of performance and energy efficiency for different instruction cache architectures. Our system-level analysis shows that the proposed design improves upon both state-of-the art private and shared cache architectures and balances well performance with energy-efficacy. On average, when executing a set of real-life IoT applications, our multi-level cache improves performance and energy efficiency both by 10% with respect to the private instruction cache system, and improves energy efficiency by 15% and 7% with a performance loss of only 2% with respect to the shared instruction cache. Besides, relaxed timing makes two-level instruction cache an attractive choice for aggressive implementation, with more slack for convergence in physical design.

In modern conditions, there is an active interest in continuing research to reduce harmful emissions of marine engine exhaust gases into the atmosphere and prepare ships for the use of technologies with zero level of these emissions. It is known that the European Green Deal concept announced by the European Union is aimed not only at the modernization of the inland vessels, and coordinated qualitative change in its composition, but within this framework also at the large-scale implementation of the energy transition as the most effective way to achieve zero emissions into the atmosphere. At the same time, environmental aspects and issues of energy efficiency are considered as a complex task of transition to energy efficient eco-navigation. To date, for the European inland navigation participants, the stages of reducing harmful emissions into the atmosphere are clearly defined and regulated with established numerical standard (reference) values for each of the components (CO, HC, NOx, PM). As for energy efficiency, discussions are still ongoing at the international level about the advisability of introducing criteria according to the principle established by the International Maritime Organization for ships based on the specific mass of harmful emissions (in terms of CO2) per unit of transport work. At the same time, there is an understanding of the specifics of navigation conditions and technologies for transporting goods, which makes us return to comparing energy efficiency and economy indicators. The team of authors made an attempt to find and offer the most comprehensive approach to assessing the energy efficiency and economy of the inland navigation vessels operation, in particular for Danube vessels, with the maximum possible consideration of the features of their operation. An integrated approach to the use of estimated indicators of the efficiency of the existing pushers with heavy convoys allows to achieve an increase in the energy efficiency of the operation of the existing fleet, as well as lead to an improvement in environmental indicators.

Summary Although previous studies point to much (untapped) potential for energy efficiency enhancement in industry, empirical research that adapts findings of environmental control to the context of energy management remains widely neglected. Specifically, previous environmental research suggests that the implementation of energy management control systems (EnMCS) could be an effective lever for companies to enhance their production systems and operations toward energy efficiency. Yet, empirical evidence for this theoretical proposition is rather missing; thus, debate continues regarding whether the high investments to set up a comprehensive EnMCS pay off in the long run. Based on a sample of 236 German manufacturing companies, this study combines primary data that capture the configuration of EnMCS with secondary data that were used to calculate energy efficiency. The results provide evidence that the extent of EnMCS implementation positively relates to firms’ energy efficiency. Findings from additional moderation analysis suggest that companies might enhance the relationship of EnMCS and energy efficiency performance by establishing a full‐time energy manager or by using external energy consulting support.

Nowadays energy efficiency (EE) of wireless communication systems has become a hot issue, yet the nonlinear effect and inefficiency of power amplifier (PA) have posed practical challenges for system designs to achieve high EE. However, most of previous work only considered linear PA. In this paper, we studies EE optimization concerning with I-way Doherty PA which has widespread use with I = 1 and I = 2 in orthogonal frequency division multiplex (OFDM)-based cognitive radio (CR) systems. The aim is to maximize EE with nonlinear PA subject to the total power budget, the interference constraint and the minimum rate requirement. Other than traditional methods, the problem is hard to solve directly due to the nonlinearity of PA, and a bisection search method tailored for nonlinear PA is adopted to achieve the sub-optimal solution. Numerical results demonstrate that if the PA's nonlinearity is not considered, the EE performance of OFDM-based CR systems can be severely overestimated by 34% at P max out = 80 W for 1-way Doherty PA. Meanwhile, the EE performance can be enhanced by approximate 32% if 2-way Doherty PA is utilized instead of 1-way Doherty PA.

A new design of solar tower system amplified with a thermoelectric unit to produce distilled water and power