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.     

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.     

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 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.     

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.     

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.     

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.     

Four-layer framework for combinatorial optimization problems domain

Four-layer framework for combinatorial optimization problems/models domain is suggested for applied problems structuring and solving: (1) basic combinatorial models and multicriteria decision making problems (e.g., clustering, knapsack problem, multiple choice problem, multicriteria ranking, assignment/allocation); (2) composite models/procedures (e.g., multicriteria combinatorial problems, morphological clique problem); (3) basic (standard) solving frameworks, e.g.: (i) Hierarchical Morphological Multicriteria Design (HMMD) (ranking, combinatorial synthesis based on morphological clique problem), (ii) multi-stage design (two-level HMMD), (iii) special multi-stage composite framework (clustering, assignment/location, multiple choice problem); and (4) domain-oriented solving frameworks, e.g.: (a) design of modular software, (b) design of test inputs for multi-function system testing, (c) combinatorial planning of medical treatment, (d) design and improvement of communication network topology, (e) multi-stage framework for information retrieval, (f) combinatorial evolution and forecasting of software, devices. The multi-layer approach covers ‘decision cycle’, i.e., problem statement, models, algorithms/procedures, solving schemes, decisions, decision analysis and improvement.     

A unified framework for heterogeneous patterns

Knowledge patterns, such as association rules, clusters or decision trees, can be defined as concise and relevant information that can be extracted, stored, analyzed, and manipulated by knowledge workers in order to drive and specialize business decision processes. In this paper we deal with data mining patterns. The ability to manipulate different types of patterns under a unified environment is becoming a fundamental issue for any ‘intelligent’ and data-intensive application. However, approaches proposed so far for pattern management usually deal with specific and predefined types of patterns and mainly concern pattern extraction and exchange issues. Issues concerning the integrated, advanced management of heterogeneous patterns are in general not (or marginally) taken into account.     

A unified framework for multimodal retrieval

In this paper, a unified framework for multimodal content retrieval is presented. The proposed framework supports retrieval of rich media objects as unified sets of different modalities (image, audio, 3D, video and text) by efficiently combining all monomodal heterogeneous similarities to a global one according to an automatic weighting scheme. Then, a multimodal space is constructed to capture the semantic correlations among multiple modalities. In contrast to existing techniques, the proposed method is also able to handle external multimodal queries, by embedding them to the already constructed multimodal space, following a space mapping procedure of a submanifold analysis. In our experiments with five real multimodal datasets, we show the superiority of the proposed approach against competitive methods.     

A unified framework for structure identification

We propose a general framework for structure identification, as defined by Dechter and Pearl. It is based on the notion of prime implicate, and handles Horn, bijunctive and affine, as well as Horn-renamable formulas, for which, to our knowledge, no polynomial algorithm has been proposed before. This framework, although quite general, gives good complexity results, and in particular we get for Horn formulas the same running time and better output size than the algorithms previously known.     

基于群能量恒定的粒子群优化算法

针对标准粒子群优化(PSO)算法在寻优过程中容易出现早熟的情况,提出一种群能量恒定的粒子群优化(SEC-PSO)算法.算法根据粒子内能进行动态分群,对较优群体采取引入最差粒子的速度更新策略,对较差群体采取带有惩罚机制的速度更新策略,由其分担由于较优群体速度降低而产生的整群能量损失,从而有效地避免了PSO算法的早熟.典型优化问题的仿真结果表明,该算法具有较强的全局搜索能力和较快的收敛速度,优化性能得到显著的提高.     

Nondifferentiable optimization algorithm for designing control systems having singular value inequalities

It has been known for some time that many control system design requirements can be expressed as differentiable inequalities. More recently, it has been shown that important structural properties such as robustness and low noise sensitivity can be expressed as nondifferentiable inequalities involving the singular values of a system or return difference transfer function matrix. This paper presents an optimization algorithm which permits all these constraints to be considered.     

A new design optimization framework based on immune algorithm and Taguchi's method

This paper describes an innovative optimization approach that offers significant improvements in performance over existing methods to solve shape optimization problems. The new approach is based on two-stages which are (1) Taguchi's robust design approach to find appropriate interval levels of design parameters (2) Immune algorithm to generate optimal solutions using refined intervals from the previous stage. A benchmark test problem is first used to illustrate the effectiveness and efficiency of the approach. Finally, it is applied to the shape design optimization of a vehicle component to illustrate how the present approach can be applied for solving shape design optimization problems. The results show that the proposed approach not only can find optimal but also can obtain both better and more robust results than the existing algorithm reported recently in the literature.