Adaptive Frequency and Biasing Techniques for Tolerance to Dynamic Temperature-Voltage Variations and Aging

Purpose – This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Design/methodology/approach – Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Findings – The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support. Research limitations/implications – Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems. Practical implications – Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support. Social implications – This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making. Originality/value – This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined AmI and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.

Efficiency of electron transport processes

Currently, the traditional magnesium oxide production process is facing exceptional challenges arising from carbon emission restrictions and environmental protection. Waste bischofite pyrolysis has attracted much attention as a promising technology to address these challenges. Nonetheless, this process has primarily been demonstrated on a laboratory scale, with limited studies on an industrial scale. A comprehensive exergy analysis was conducted for the entire process and individual subunits within the pyrolysis process to identify potential areas for process enhancement. A FORTRAN subroutine based on empirical correlations of pyrolysis product yields was developed considering the impact of decomposition reactions on the simulation. Furthermore, the optimization of energy and exergy efficiency of the system was discussed in terms of the carbon dioxide emission factor, equivalence ratio, and pyrolysis temperature. The results show that the primary energy bottleneck lies in the combustion phase. In addition, the optimal energy and exergy efficiency conditions are a carbon dioxide emission factor of 5.3, an equivalent ratio of 1.15, and a pyrolysis temperature of 1100 °C. In comparison to the pilot-scale conditions, the energy efficiency and exergy efficiency increase by 2.55 and 3.61%, respectively. At this time, the MgO yield is 100%, and the HCl concentration is above 9.33%.

This paper has proposed an adaptive antenna selection technique in multiple input multiple output (MIMO) system that optimizes the system energy efficiency to achieve maximum throughput. Though throughput increases with the dimension of MIMO system, the energy consumed per RF module is a major concern. The antenna selection technique exploits the channel state information (CSI) at transmitting end to reduce the number of RF module to achieve maximum benefit out of MIMO system. However, simultaneously achievement of best throughput and energy efficiency, with fixed number of transmitted antennas, is not possible. In this paper, the number of transmitting antenna to be selected is varied as per channel condition. The proposed technique aims to maximize the instantaneous throughput with optimum energy efficiency.

Heat Treatment Process Energy Efficient Design and Optimisation

Variations, both static and dynamic in nature, impact the performance and power-efficiency of microprocessors and other digital integrated circuits. While static process variations can often be mitigated through binning or by post-silicon tuning, dynamic variations change as a function of time and environment and cannot be reduced by static tuning. Examples of these types of dynamic variations include power supply (V CC ) droop, temperature change, and device aging over time. The droop magnitude, frequency and duration depend on the complex interaction of capacitive and inductive parasitics at the board, package, and die levels and change with the workload and current demand [1-3]. V CC droops contain high-frequency (i.e., fast changing) and low-frequency (i.e., slow changing) components and occur locally and/or globally across the die. Temperature variations occur at a relatively slower time scale with local hot spots on the die, depending on environmental and workload conditions as well as the heat-removal capability of the package. Further, transistor aging slowly degrades the drive current over time as a function of gate bias and temperature conditions. Conventional designs build in V CC guardband (V CC GB) at a target frequency to ensure correct functionality even in the presence of worst-case dynamic variations. In the digital core, this GB accounts for the delay increase on critical paths when dynamic variations occur and guarantees that timing constraints are met even under worst-case conditions. In the storage arrays (register files and caches) this GB is added to the static minimum operating voltage (V MIN ) to obtain the resultant operating V MIN and results in an increase of both leakage and dynamic array power. The key design aspect of adaptive and resilient designs is to remove a part of this GB, such that logic timing and memory functionality can be met at a lower V CC . This improves energy efficiency and improves battery life; a key consideration in mobile and handheld platforms. The mitigation of the design GB can be achieved in two fundamentally different ways. For dynamic variations with time scales in the order of hundreds of μs to ms (such as temperature variations or transistor aging), a reactive scheme where any functional error is avoided is applicable. For example, embedded temperature sensors can be used to trigger changes in the clock frequency or changes in the supply voltage, such that at low temperatures (when transistor currents are higher) a faster clock (at iso-VCC) or a lower supply (at iso-clock frequency) can be employed [4]. For dynamic changes which occur in time scales of ns to μs, such reactive schemes are impossible by design. Hence, an alternative scheme can be employed where timing errors (in logic) or read-write errors (in memory) are allowed to happen. Once errors occur, they are detected, and the errant instruction is replayed with proper care such that data integrity is maintained. A combination of such adaptive schemes (Fig. 1) result in the mitigation of a major part of the design GB, resulting in higher energy efficiency.

Energy efficiency evaluation of industrial park customers is carried out to help customers develop optimization strategies and improve the energy efficiency level. The evaluation indexes are proposed based on pressure-state-response (PSR) model and the redundant indexes are selected by combining principal component analysis (PCA) and correlation analysis. Thus the dynamic energy efficiency index system is constructed. The weight of indexes is calculated by the entropy weight method (EWM). An energy efficiency evaluation model based on improved Grey-technique for order preference by similarity to ideal solution (TOPSIS) is proposed, which replaces the Euclidean distance by weighted grey correlation degree and can accurately evaluate customers’ energy efficiency level. An energy efficiency optimization model is proposed, which can maximize optimization benefits while improving energy efficiency. The validity of the evaluation and optimization models is verified by the case study of an industrial park.

Cell discontinuous transmission (DTX) has been proposed as a solution to reduce the energy consumption of cellular networks. This paper investigates the impact of network traffic load on the spectral and energy efficiency of cellular networks with DTX. The signal-to-interference-plus-noise ratio (SINR) distribution as a function of traffic load is derived first. Then, the sufficient condition for ignoring thermal noise and simplifying the SINR distribution is investigated. Based on the simplified SINR distribution, the network spectral and energy efficiency as functions of network traffic load are derived. It is shown that the network spectral efficiency increases monotonically in traffic load, while the optimal network energy efficiency depends on the ratio of the sleep-mode power consumption to the active-mode power consumption of base stations. If the ratio is larger than a certain threshold, the network energy efficiency increases monotonically with network traffic load and is maximized when the network is fully loaded. Otherwise, the network energy efficiency first increases and then decreases in network traffic load. The optimal load can be identified with a binary search algorithm. The power ratio threshold depends solely on the path loss exponent α, e.g., 56% for α = 4. All these analytic results are further validated by the numerical simulations.

In this paper we examine the expectations and limitations of design technologies such as adaptive voltage scaling (AVS) and adaptive body biasing (ABB) in a modern deep sub-micron process. To serve this purpose, a set of ring oscillators was fabricated in a 90nm triple-well CMOS technology. The analysis hereby presented is based on two ring oscillators running at 822MHz and 93MHz, respectively. Measurement results indicate that it is possible to reach 13.8x power savings by 3.4x frequency downscaling using AVS, ±11% power and ±8% frequency tuning at nominal conditions using ABB only, 22x power savings with 5x frequency downscaling by combining AVS and ABB, as well as 22x leakage reduction.

Energy efficiency (EE) optimization is investigated for a multi-user cognitive radio network (CRN) over multiple-input-multiple-output (MIMO) interference channels (ICs). To reduce the system overhead due to information exchange among the secondary CR uses (SUs), the EE optimization problem is formulated as a non-cooperative game, where each SU transmitter competes against the other SU pairs by optimizing its transmit covariance matrix. Specifically, each multi-antenna SU maximizes locally its energy efficiency in terms of the number of bits transmitted per unit energy consumption, subject to the per-SU transmit power constraint and the primary user (PU) perceived total interference constraint. It is proved that the formulated non-cooperative game admits at least one Nash equilibrium (NE), and the sufficient condition for a unique NE is derived subsequently. Primal decomposition is employed in the local EE optimization problem to relax the coupling PU perceived interference constraint such that fully distributed operation is allowed. A distributed iterative EE optimization algorithm (DIEEOA) is then proposed to obtain the unique NE, which is shown to converge to the global optimum. Linear precoding techniques are employed to mitigate the impacts of multi-user interference and imperfect channel state information (CSI). Through numerical simulations, effectiveness of the proposed scheme is validated and the system setting parameters' impacts on the performance are studied.

Next-generation wireless communications are expected to provide reliable high-rate connectivity. The recent concept of fog-enabled architecture comes to rescue by invoking a substantial amount of data storage close to end-user devices for meeting these challenges. Thus, caching popular contents at heterogeneous devices, e.g., fog nodes (FNs) or fog access points (FAPs), constitutes a promising technique of reducing both the traffic and the energy consumption of the backhaul links. Therefore, in this paper, we propose an energy-efficient caching and node association algorithm for cache-aided fog networks. First, we solve the problem of energy-efficient content caching and delivery in the FNs/FAPs in conjunction with a fixed node association strategy, where the FNs communicate either with other FNs by node-to-node (N2N) communications or with the FAPs in their proximity. In both caching scenarios, we investigate the relationship between the caching probability of the file and the energy-efficient content delivery by formulating the associated energy efficiency (EE) optimization problem under caching memory constraints. Then, we derive a joint modulation mode allocation strategy and caching policy for each content caching node and conceive a joint node association and caching algorithm for maximizing the EE. Finally, we quantify both the overall EE and throughput for demonstrating that the proposed caching and transmission strategy achieves significant performance improvements.

The present current study aimed at analyzing the input–output energies and optimizing energy efficiency and greenhouse gases (GHG) in crops production at the northeast of Iran. The results of this research showed that the total energy inputs were equal to 42.4, 28.6, and 72.8 GJha−1 for, wheat, barley, and sugar beet, respectively. It was also shown that the highest energy efficiency, the lowest GHG emissions, and GHG intensity all belonged to barley production. The highest percentage of GHG intensity pertained to sugar beet production (41%), which was followed by wheat (38%) and barley (21%). It was also revealed that GHG intensity is a better criterion for the study of GHG since it also makes use of the energy output information in crops. The optimal model responses for the crop type and proper cultivation planning was 284432 hectares in terms of barley production, while those responses for wheat and sugar beet were zero and, thereby, they were eliminated from the model of Multiple Goal Programming (MGP). In the same way, this model could increase energy efficiency to 444875, while GHG emissions and its intensity decreased by 213259 tonCO2eq and 1.3 tonCO2eq MJoutput−1 per year, respectively.

A two-stage slicer employing body biasing for 64-Gb/s PAM4 wireline receiver in 22-nm FDSOI technology

Optimizing energy efficiency in data center cooling towers is crucial for reducing operational costs and environmental impact. This review explores the integration of predictive maintenance and project management to achieve this goal. By leveraging predictive maintenance techniques, data center operators can anticipate and address potential issues before they lead to costly downtime or inefficiencies. Project management plays a key role in coordinating these efforts, ensuring that maintenance activities are carried out efficiently and effectively. Predictive maintenance relies on data analytics and machine learning algorithms to monitor the condition of cooling towers in real-time. By analyzing data such as temperature, pressure, and flow rates, these algorithms can detect anomalies and predict potential failures. This proactive approach allows data center operators to schedule maintenance activities during planned downtime, minimizing disruptions to operations. Project management practices, such as Agile or Waterfall methodologies, are essential for coordinating predictive maintenance efforts. Project managers oversee the planning, execution, and monitoring of maintenance activities, ensuring that they are completed on time and within budget. They also facilitate communication between stakeholders, including maintenance teams, data analysts, and management, to ensure that everyone is aligned and working towards the same goals. By integrating predictive maintenance with project management, data center operators can achieve significant improvements in energy efficiency. By addressing maintenance issues proactively, operators can reduce energy consumption, extend the lifespan of cooling equipment, and minimize downtime. Additionally, project management practices ensure that these efforts are coordinated and effective, maximizing the benefits of predictive maintenance. In conclusion, optimizing energy efficiency in data center cooling towers requires a holistic approach that combines predictive maintenance and project management. By leveraging predictive maintenance techniques and project management practices, data center operators can achieve significant improvements in energy efficiency, reduce operational costs, and enhance environmental sustainability.

Recently, FPGA-based CNN accelerators have demonstrated superior energy efficiency compared to high-performance devices like GPGPUs. However, due to the constrained on-chip resource and many other factors, single-board FPGA designs may have difficulties in achieving optimal energy efficiency. In this paper we present a deeply pipelined multi-FPGA architecture that expands the design space for optimal performance and energy efficiency. A dynamic programming algorithm is proposed to map the CNN computing layers efficiently to different FPGA boards. To demonstrate the potential of the architecture, we built a prototype system with seven FPGA boards connected with high-speed serial links. The experimental results on AlexNet and VGG-16 show that the prototype can achieve up to 21x and 2x energy efficiency compared to optimized multi-core CPU and GPU implementations, respectively.