A generic energy optimization framework for heterogeneous platforms using scaling models

Mobile platforms are becoming highly heterogeneous by combining a powerful multiprocessor system-on-a-chip (MpSoC) with numerous other resources, including display, memory, power management IC, battery and wireless modems into a compact package. Furthermore, the MpSoC itself is a heterogeneous resource that integrates many processing elements such as CPU cores, GPU, video, image, and audio processors. Platform energy consumption and responsiveness are two major considerations for mobile systems, since they determine the battery life and user satisfaction, respectively. As a result, energy minimization approaches targeting mobile computing need to consider the platform at various levels of granularity. In this paper, we first present power consumption, response time, and energy consumption models for mobile platforms. Using these models, we optimize the energy consumption of baseline platforms under power, response time, and thermal constraints with and without introducing new resources. Finally, we validate the proposed framework through experiments on Qualcomm’s Snapdragon 800 Mobile Development Platforms.     

Electronic design automation using a unified optimization framework

This work proposes an object-oriented unified optimization framework (UOF) for general problem optimization. Based on biological inspired techniques, numerical deterministic methods, and C++ objective design, the UOF itself has significant potential to perform optimization operations on various problems. The UOF provides basic interfaces to define a general problem and generic solver, enabling these two different research fields to be bridged. The components of the UOF can be separated into problem and solver components. These two parts work independently allowing high-level code to be reused, and rapidly adapted to new problems and solvers. The UOF is customized to deal with several optimization problems. The first experiment involves a well-known discrete combinational problem, wihle the second one studies the robustness for the reverse modeling problem, which is in high demanded by device manufacturing companies. Additionally, experiments are undertaken to determine the capability of the proposed methods in both analog and digital circuit design automation. The final experiment designs antenna for rapidly growing wireless communication. Most experiments are categorized as simulation-based optimization tasks in the microelectronics industry. The results confirm that UOF has excellent flexibility and extensibility to solve these problems successfully. The developed open-source project is publicly available.     

A middleware framework for application-aware and user-specific energy optimization in smart mobile devices

Mobile battery-operated devices are becoming an essential instrument for business, communication, and social interaction. In addition to the demand for an acceptable level of performance and a comprehensive set of features, users often desire extended battery lifetime. In fact, limited battery lifetime is one of the biggest obstacles facing the current utility and future growth of increasingly sophisticated “smart” mobile devices. This paper proposes a novel application-aware and user-interaction aware energy optimization middleware framework (AURA) for pervasive mobile devices. AURA optimizes CPU and screen backlight energy consumption while maintaining a minimum acceptable level of performance. The proposed framework employs a novel Bayesian application classifier and management strategies based on Markov Decision Processes and Q-Learning to achieve energy savings. Real-world user evaluation studies on Google Android based HTC Dream and Google Nexus One smartphones running the AURA framework demonstrate promising results, with up to 29% energy savings compared to the baseline device manager, and up to 5×savings over prior work on CPU and backlight energy co-optimization.     

混合优化策略统一结构的探讨

算法混合已成为提高优化性能和效率的一个重要而有效的途径。围绕meta-heuristic算法，通过对混合优化算法及其结构的归类与综述，提出了混合优化算法的一种统一结构，并对基若干问题进行分析探讨，为混合算法的设计与应用提供一定的指导性原则。     

Topology optimization of energy harvesting devices using piezoelectric materials

Energy harvesting devices based on the piezoelectric effect that converts ambient energy to electric energy is a very attractive energy source for remote sensors and embedded devices. Although topology optimization has been applied to the design of piezoelectric transducers, the locations of piezoelectric materials are predefined and only the optimal layout of elastic materials is considered. In this paper, both elastic materials as well as piezoelectric materials are considered for the design of energy harvesting devices under the topology optimization formulation. The objective function for this study is to maximize the energy conversion factor. The sensitivities of both stored strain energy and electrical energy are derived by the adjoint method. Examples of energy harvesting devices are presented and discussed using the proposed method.     

Solo—system for on-line optimization

SOLO (system for on-line optimization) is a system of interactive computer programs for solving problems of optimization. It exists in two forms, one of which uses a teletypewriter exclusively for both input and output. The other retains the teletypewriter for input but presents outputs mainly on a cathode ray tube display. The optimization problem is expressed as the maximization of a function of an arbitrary bumber of parameters. Optimization problems often occur in engineering design, where it is often a matter of fitting a computed output, response or performance curve to the one desired. SOLO can be regarded as a tool for computer-aided design in this sense. SOLO does not make use of any methods of numerical analysis, but relies instead on organized interactive searching. No function derivatives are computed, so that SOLO can be applied to any type of function. Experience so far suggests that SOLO is much more efficient with functions possessing many local optima than it is with functions which have just one maximum.     

A unified framework for designing high performance in-memory and hybrid memory file systems

The emerging non-volatile memory technologies provide a new choice for storing persistent data in memory. Therefore, file system structure needs re-studying and re-designing. Our goal is to design a framework that gives high-performance in-memory file accesses and allows a file whose data can be stored across memory and block device. This paper presents a novel unified framework for in-memory and hybrid memory file systems based on the concept that each file has a contiguous “File Virtual Address Space”. Within this framework, the file access for in-memory data can be efficiently handled by address translation hardware and the virtual address space of file. The file accesses for data in block devices are handled by a dedicated page fault handler for file system. A file system called Hybrid Memory File System (HMFS) is implemented based on this framework. Experimental results show that the throughput of HMFS approaches the memory bus bandwidth in best cases. Compared with in-memory file systems, HMFS reaches 5 times, 2.1 times, and 1.6 times faster than EXT4 on Ramdisk, RAMFS, and PMFS, respectively. Compared with EXT4 on SSD and EXT4 using page cache, HMFS also achieves 100 times and tens of times performance improvement, respectively.     

Metamodel-based collaborative optimization framework

This paper focuses on the metamodel-based collaborative optimization (CO). The objective is to improve the computational efficiency of CO in order to handle multidisciplinary design optimization problems utilising high fidelity models. To address these issues, two levels of metamodel building techniques are proposed: metamodels in the disciplinary optimization are based on multi-fidelity modelling (the interaction of low and high fidelity models) and for the system level optimization a combination of a global metamodel based on the moving least squares method and trust region strategy is introduced. The proposed method is demonstrated on a continuous fiber-reinforced composite beam test problem. Results show that methods introduced in this paper provide an effective way of improving computational efficiency of CO based on high fidelity simulation models.     

随机仿真优化的一类遗传序优化框架

针对仿真优化问题存在随机性、计算费时、解空间巨大、多极小等难点,结合遗传算法的并行遗传搜索、最优计算量分配以及序优化的目标软化和序比较思想提出一类遗传序优化框架,进而讨论了该方法的收敛性和具体实施问题,最后指出了进一步的研究内容.     

A global energy optimization framework for 2.1D sketch extraction from monocular images

The 2.1D sketch is a layered image representation, which assigns a partial depth ordering of over-segmented regions in a monocular image. This paper presents a global optimization framework for inferring the 2.1D sketch from a monocular image. Our method only uses over-segmented image regions (i.e., superpixels) as input, without any information of objects in the image, since (1) segmenting objects in images is a difficult problem on its own and (2) the objective of our proposed method is to be generic as an initial module useful for downstream high-level vision tasks. This paper formulates the inference of the 2.1D sketch using a global energy optimization framework. The proposed energy function consists of two components: (1) one is defined based on the local partial ordering relations (i.e., figure-ground) between two adjacent over-segmented regions, which captures the marginal information of the global partial depth ordering and (2) the other is defined based on the same depth layer relations among all the over-segmented regions, which groups regions of the same object to account for the over-segmentation issues. A hybrid evolution algorithm is utilized to minimize the global energy function efficiently. In experiments, we evaluated our method on a test data set containing 100 diverse real images from Berkeley segmentation data set (BSDS500) with the annotated ground truth. Experimental results show that our method can infer the 2.1D sketch with high accuracy.     

A unified framework for chaotic neural-network approaches to combinatorial optimization.

As an attempt to provide an organized way to study the chaotic structures and their effects in solving combinatorial optimization with chaotic neural networks (CNN), a unifying framework is proposed to serve as a basis where the existing CNN models ran be placed and compared. The key of this proposed framework is the introduction of an extra energy term into the computational energy of the Hopfield model, which takes on different forms for different CNN models, and modifies the original Hopfield energy landscape in various manners. Three CNN models, namely the Chen and Aihara model with self-feedback chaotic simulated annealing [CSA] (1995, 1997), the Wang and Smith model with timestep CSA (1998), and the chaotic noise model, are chosen as examples to show how they can be classified and compared within the proposed framework.     

FOPS: A new framework for the optimization with variable number of dimensions

A single‐ and multi‐objective optimization package is presented and described in detail. It contains an ensemble of local and global optimization routines. Procedures controlling variable number of dimensions are implemented as well, which is a rare feature among optimization oriented packages. The package is provided as a MATLAB toolbox. It excels in versatility and extensibility, which is demonstrated on a series of examples covering classical electromagnetism and antenna design. It is taken for granted that defining parameters of the optimization method can be set prior to the simulation run. However, its effective performance can be changed during the optimization run thanks to the full control feature. Moreover, it opens new possibilities in merging various algorithms into hybrids, performing complex dynamic programming tasks, or exploiting third party software. These advantages render the package as a perfect tool to deal with nowadays challenging engineering tasks.     

Gamification: A framework for designing software in e-banking

The gamification software development gave emphasis to the role played by the users to test and improve the software. This study presents a framework for software gamified in e-banking, taking a users' groups and a qualitative research approach, to check the users' design preferences in five cases of banking software gamified (Futebank, Dreams, Galaxy, Olympics, and Warrants). After software presentation, and usage experience, 53 participants, responses to a survey with six open questions. The data were analyzed through a text semantic software, to detect and classify lexical items in, accordance, with standard of software quality characteristics and user experiences. Two primary categories were identified, as well five dimensions in each element and characteristic categories. The results show five characteristic dimensions (design, appearance, functionality, rules, and objectives) and five element dimension (game, product, security, process, and information). These findings provide a framework for web designers and e-business, highlighting the most important software features when dealing with serious applications with game design. It adds value to the current literature on understanding the customer relationship with the game and the financial product, identifying new dimensions (game and product) on the approach of thinking and design gamification in e-banking. Our finding contributes to a better understanding of key elements and characteristics in e-banking software design and has important practical implications for software development and marketing practices. Thoughts on the users' software design preferences identified, should propel increase adoption and attractiveness of online banking.     

An optimization framework for the energy management of carrier ethernet networks with Multiple Spanning Trees

We propose an energy management framework to optimize the energy consumption of networks using the Multiple Spanning Tree Protocol such as Carrier Grade Ethernet networks. The objective is to minimize the energy consumption of nodes and links while considering QoS constraints. The energy management is done through the Multiple Spanning Tree Protocol (MSTP) by choosing from a given set the most appropriate Spanning Trees and the most appropriate edges to operate while respecting the traffic demands. A trade-off framework between energy consumption and network performance is proposed. Results show that it is possible to achieve a good traffic engineering while operating the network closer to the minimum energy value.     

Towards a unified multi-source-based optimization framework for multi-label learning

In the era of Big Data, a practical yet challenging task is to make learning techniques more universally applicable in dealing with the complex learning problem, such as multi-source multi-label learning. While some of the early work have developed many effective solutions for multi-label classification and multi-source fusion separately, in this paper we learn the two problems together, and propose a novel method for the joint learning of multiple class labels and data sources, in which an optimization framework is constructed to formulate the learning problem, and the result of multi-label classification is induced by the weighted combination of the decisions from multiple sources. The proposed method is responsive in exploiting the label correlations and fusing multi-source data, especially in the fusion of long-tail data. Experiments on various multi-source multi-label data sets reveal the advantages of the proposed method.     

A unified framework for document restoration using inpainting and shape-from-shading

We present a restoration framework to reduce undesirable distortions in imaged documents. Our framework is based on two components: (1) an image inpainting procedure that can separate non-uniform illumination (and other) artifacts from the printed content and (2) a shape-from-shading (SfS) formulation that can reconstruct the 3D shape of the document's surface. Used either piecewise or in its entirety, this framework can correct a variety of distortions including shading, shadow, ink-bleed, show-through, perspective and geometric distortions, for both camera-imaged and flatbed-imaged documents. Our overall framework is described in detail. In addition, our SfS formulation can be easily modified to target various illumination conditions to suit different real-world applications. Results on images of synthetic and real documents demonstrate the effectiveness of our approach. OCR results are also used to gauge the performance of our approach.     

2D decision-making for multicriteria design optimization

The high dimensionality encountered in engineering design optimization due to large numbers of performance criteria and specifications leads to cumbersome and sometimes unachievable trade-off analyses. To facilitate those analyses and enhance decision-making and design selection, we propose to decompose the original problem by considering only pairs of criteria at a time, thereby making trade-off evaluation the simplest possible. For the final design integration, we develop a novel coordination mechanism that guarantees that the selected design is also preferred for the original problem. The solution of an overall large-scale problem is therefore reduced to solving a family of bicriteria subproblems and allows designers to effectively use decision-making in merely two dimensions for multicriteria design optimization.

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Margaret M. Wiecek is on leave from the Department of Mathematical Sciences, Clemson University, South Carolina 29634, USA.     

A web-based software tool for participatory optimization of conservation practices in watersheds

WRESTORE (Watershed Restoration Using Spatio-Temporal Optimization of Resources) is a web-based, participatory planning tool that can be used to engage with watershed stakeholder communities, and involve them in using science-based, human-guided, interactive simulation–optimization methods for designing potential conservation practices on their landscape. The underlying optimization algorithms, process simulation models, and interfaces allow users to not only spatially optimize the locations and types of new conservation practices based on quantifiable goals estimated by the dynamic simulation models, but also to include their personal subjective and/or unquantifiable criteria in the location and design of these practices. In this paper, we describe the software, interfaces, and architecture of WRESTORE, provide scenarios for implementing the WRESTORE tool in a watershed community's planning process, and discuss considerations for future developments.