Energy optimization in memory address bus structure for application-specific systems

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.

We consider the Minimum Elmore Delay Steiner Tree Problem, which is a key problem in VLSI design: We are given a set of pins which have to be connected by a Steiner tree. One of the pins is the source. Challenging timing constraints impose tight bounds on the delay of propagating a signal from the source to the other pins. The commonly used measure is Elmore delay (Elmore in J Appl Phys 19:55---63, 1948). We consider two variants: minimizing the maximum Elmore delay or a weighted sum of Elmore delays. Both variants are strongly NP-hard even for very restricted special cases. Although it is a central problem in VLSI design (Kahng and Robins in On optimal interconnections for VLSI. Kluwer, Boston, 1995; Korte and Vygen in Building bridges--between mathematics and computer science. Springer, Berlin, pp 333---368, 2008), no approximation algorithms were known so far. In this work, we give the first constant-factor approximation algorithm. It works for both variants. The algorithm achieves an approximation ratio of 3.39 in the rectilinear plane and 4.11 in general metric spaces. We can show that our algorithm is best possible in a certain sense. We also demonstrate that our algorithm leads to improvements on real world VLSI instances compared to the currently used standard method of computing short Steiner trees.

Gas turbine combustion: Arthur H. Lefebvre, Hemisphere Publishing, Washington, D. C., 1983, xvii + 531 pp, $38.50

The coffee processing industry is amongst the most energy-intensive industrial sectors, with the roasting process requiring substantial quantities of energy. To enhance sustainability, various energy optimization and recovery methods have been proposed. This study evaluates the environmental benefits of integrating energy recovery techniques in a typical coffee processing industry using a Life Cycle Assessment (LCA) approach. Specifically, two alternative scenarios were compared to a baseline processing line; (i) Scenario A, which involves recycling hot air streams to reduce natural gas consumption, and (ii) Scenario B, which utilizes an Organic Rankine Cycle (ORC) to convert waste heat to electricity. The LCA analysis indicated that Scenario A achieved a 25% reduction in greenhouse gas emissions and an 18% decrease in fossil fuel use. Scenario B demonstrated even greater environmental benefits, with a 40% reduction in greenhouse gas emissions and a 36% decrease in fossil fuel depletion. These findings underline the potential of integrating energy recovery technologies to enhance the sustainability of coffee production, offering valuable insights for industry stakeholders and researchers focused on sustainable manufacturing practices.

For sustainable manufacturing activities, industries that use 6-DOF industrial robots (IR) should consider the energy efficiency of their IR.For example, it has been estimated that on average 8% of the total energy consumption in the vehicles' lifecycle production is consumed by the robots (electrical energy).Nevertheless, IR are used for different tasks such as material handling, assembly, painting, welding, etc., and plays a fundamental role in industries nowadays.In this paper, an energy consumption model is implemented to optimize the energy consumption of a 6-DOF IR.In this paper, the UR5e has been used.For energy optimization, an iterative approach has been proposed, whereby the programmer optimizes the trajectory by rechecking the energy consumption produced by certain joint configurations.The results show different power consumptions for different configurations from which the programmer can use an adequate configuration that allows the execution of the task.The iterative approach is applicable to a serial kinematic robot arm with n-DOF.(free space) (14 pt)

Some polynomially solvable subcases of the detailed routing problem in VLSI design

A hundred fold performance improvement of a CAD tool can bring about a similar increase in design size. Hardware accelerators for CAD tools offer such performance improvements, but are application specific. Though some accelerators can be programmed to adapt themselves to somewhat different applications, it takes as much time and money to reconfigure one as it does to purchase a new one. Hence the use and acceptance of hardware accelerators has been limited. To alleviate the above mentioned problem we demonstrate that a wide variety of problems in VLSI design, viz gate array placement, standard cell placement, PLA folding, channel routing can be mapped on to a single mother problem. From the solution to the mother problem, the solutions to the other problems can be deduced. Therefore, a single hardware accelerator to solve the afore mentioned mother problem along with a translator-deducer kernel to translate the various problems to the mother problem and again decipher the solution would be sufficient to ...

Distributed Genetic Algorithms (DGAs) designed for the Internet have to take its high communication cost into consideration. For island model GAs, the migration topology has a major impact on DGA performance. This paper describes and evaluates an adaptive migration topology optimizer that keeps the communication load low while maintaining high solution quality. Experiments on benchmark problems show that the optimized topology outperforms static or random topologies of the same degree of connectivity. The applicability of the method on real-world problems is demonstrated on a hard optimization problem in VLSI design.

Timing optimization is one of the most significant problems in VLSI design because it determines performance of the designed circuit. Such optimization problems contain hundreds of thousands of variables therefore they may be classified as high performance computing problems. In this paper we present the results of our parallel implementation of two popular approaches to integrated circuit timing optimization. Both approaches are based on the analytical placement computational scheme. The first approach is iterative net-weighting placement. The second executes buffer insertion algorithm after the placement cycle. We analyze effectiveness of both approaches, discuss their drawbacks and suggest ways of improvement.KeywordsVLSI CADtiming-driven designphysical design algorithmsplacementbufferingstatic timing analysismulticore architecture

Carbon-free energy optimization in intelligent communities considering demand response

Today, the stability of the electric power grid is maintained through real time balancing of generation and demand. Grid scale energy storage systems are increasingly being deployed to provide grid operators the flexibility needed to maintain this balance. Energy storage also imparts resiliency and robustness to the grid infrastructure. Over the last few years, there has been a significant increase in the deployment of large scale energy storage systems. This growth has been driven by improvements in the cost and performance of energy storage technologies and the need to accommodate distributed generation, as well as incentives and government mandates. Energy management systems (EMSs) and optimization methods are required to effectively and safely utilize energy storage as a flexible grid asset that can provide multiple grid services. The EMS needs to be able to accommodate a variety of use cases and regulatory environments. In this paper, we provide a brief history of grid-scale energy storage, an overview of EMS architectures, and a summary of the leading applications for storage. These serve as a foundation for a discussion of EMS optimization methods and design.

Indoor comfort parameters and outdoor environmental parameters are adjusted by controlling the compressor speed to adjust the refrigerant circulation and flow rate entering the indoor heat exchanger, timely meeting the indoor cooling and heating load requirements, and achieving summer cooling and winter heating. The problem of energy waste caused by the operation of CAC (Central Air Conditioning) systems also comes with it. In this regard, this article integrates DL (Deep Learning) to design energy consumption prediction and optimization algorithms for CAC systems. The results indicate that the energy consumption varies with the equipment speed at different optimal settings, and the overall energy consumption optimization effect is significant. Without optimization control, the average hourly power consumption was 264.7kw, and after optimization, the average hourly power consumption was 252.4kw. However, in the testing of system efficiency, it can be found that the data comparison before and after improving system efficiency optimization shows that the system efficiency improved by about 5% after using the algorithm in this paper. This article aims to help managers control energy consumption, save costs, and ultimately achieve the goal of reducing operational management costs through the study of optimizing energy consumption control in CAC systems using DL. Indoor comfort parameters and outdoor environmental conditions are regulated by adjusting the compressor speed to control the refrigerant circulation and flow rate entering the indoor heat exchanger. This ensures the timely fulfillment of indoor cooling and heating load requirements, enabling effective summer cooling and winter heating. Additionally, it addresses the issue of energy wastage resulting from the operation of CAC systems. In response to this challenge, this paper integrates DL techniques to develop energy consumption prediction and optimization algorithms tailored for CAC systems. The findings reveal that energy consumption fluctuates with equipment speed under various optimal settings, demonstrating a substantial overall improvement in energy consumption optimization. In the absence of optimization control, the average hourly power consumption stood at 264.7 kW, which decreased to 252.4 kW after implementing optimization measures. However, during the system efficiency testing, it was observed that the comparison of data before and after implementing system efficiency optimization revealed an approximate 5% enhancement in system efficiency through the utilization of the algorithm presented in this paper. This article's objective is to assist managers in effectively managing energy consumption, reducing costs, and ultimately achieving the goal of minimizing operational management expenses by investigating energy consumption optimization in CAC systems through the application of DL.

Heating, ventilation and air conditioning (HVAC) chilled water systems offer significant potential for energy saving and reinforcement learning (RL) methods have been extensively studied and validated for optimizing HVAC energy consumption. However, RL's low sample efficiency and reliance on randomized exploration limit its practical application. To enhance the robustness and stability of RL‐based energy optimization methods, an RL optimization approach based on a mechanism‐data hybrid‐driven model is proposed, derived from the MBPO (Model‐based policy optimization) scheme. Firstly, a novel end‐to‐end HVAC chilled water system model is developed to serve as the foundation for the hybrid model design. Second, a hybrid‐driven RL environment model framework is introduced, combining a mechanistic model with a probabilistic neural network. The mechanistic component provides generalization capabilities, while the data‐driven component offers adaptability. Third, improvements to MBPO are proposed, including double policy optimization and adaptive branch rollout, further to enhance dynamic environmental adaptability and model utilization efficiency. Finally, comparative and ablation experiments conducted using both simulation environments and measured data demonstrate that the proposed method achieves higher learning efficiency and improved robustness.

Two major problems are involved in VLSI design, namely, the placement of components and routing between these components. Single row routing problem is a combinatorial optimization problem of significant importance for the design of complex VLSI multi layer printed circuit boards (PCB's). The design involves conductor routing that makes all the necessary wiring and interconnections between the PCB modules, such as pins, vias, and backplanes. In very large systems, the number of interconnections may exceed tens of thousands. Therefore, we have to optimize the wire routing and interconnections and thus determine the efficient designs. Kernighan-Lin algorithm, traveling salesman problem, simulated annealing algorithm and single row routing problem are used to find the best design. Included here are some simple examples to find the results. A simulation program using Microsoft Visual C++ is developed to simulate the single row routing problem using the simulated annealing algorithm. Keywords: Placement and Routing in VLSI; Single Row Routing; Kernighan-Lin Algorithm; Simulated Annealing

Multiple-input multiple-output (MIMO) technology is a breakthrough that considerably improves the throughput of wireless networks. The ever-growing technology has improved the smartphones to meet the requirements of current IEEE standards for wireless local area networks (WLANs). However, the benefits of smartphones are always constrained by their battery life. This study investigates the energy consumed by a smartphone during the transmission and reception of data. The effect of the different features of 802.11n (including Modulation and Coding Scheme (MCS) and MIMO) is investigated in terms of throughput and energy consumption using a network simulator (NS3). Additionally, the energy consumption during the process of transmission and reception is compared by using per-bit energy consumption for data transfer with different MIMO configurations and physical data rates. The results show that increasing the system configuration beyond 2×2 MIMO improves the throughput and reduce the energy consumption. In addition, a simple energy consumption model is developed for transmission and reception based on MIMO parameter.