3,778 research outputs found

    Considering power variations of DVS processing elements for energy minimisation in distributed systems

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    Evolution of constrained layer damping using a cellular automaton algorithm

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    Constrained layer damping (CLD) is a highly effective passive vibration control strategy if optimized adequately. Factors controlling CLD performance are well documented for the flexural modes of beams but not for more complicated mode shapes or structures. The current paper introduces an approach that is suitable for locating CLD on any type of structure. It follows the cellular automaton (CA) principle and relies on the use of finite element models to describe the vibration properties of the structure. The ability of the algorithm to reach the best solution is demonstrated by applying it to the bending and torsion modes of a plate. Configurations that give the most weight-efficient coverage for each type of mode are first obtained by adapting the existing 'optimum length' principle used for treated beams. Next, a CA algorithm is developed, which grows CLD patches one at a time on the surface of the plate according to a simple set of rules. The effectiveness of the algorithm is then assessed by comparing the generated configurations with the known optimum ones

    An efficient design space exploration framework to optimize power-efficient heterogeneous many-core multi-threading embedded processor architectures

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    By the middle of this decade, uniprocessor architecture performance had hit a roadblock due to a combination of factors, such as excessive power dissipation due to high operating frequencies, growing memory access latencies, diminishing returns on deeper instruction pipelines, and a saturation of available instruction level parallelism in applications. An attractive and viable alternative embraced by all the processor vendors was multi-core architectures where throughput is improved by using micro-architectural features such as multiple processor cores, interconnects and low latency shared caches integrated on a single chip. The individual cores are often simpler than uniprocessor counterparts, use hardware multi-threading to exploit thread-level parallelism and latency hiding and typically achieve better performance-power figures. The overwhelming success of the multi-core microprocessors in both high performance and embedded computing platforms motivated chip architects to dramatically scale the multi-core processors to many-cores which will include hundreds of cores on-chip to further improve throughput. With such complex large scale architectures however, several key design issues need to be addressed. First, a wide range of micro- architectural parameters such as L1 caches, load/store queues, shared cache structures and interconnection topologies and non-linear interactions between them define a vast non-linear multi-variate micro-architectural design space of many-core processors; the traditional method of using extensive in-loop simulation to explore the design space is simply not practical. Second, to accurately evaluate the performance (measured in terms of cycles per instruction (CPI)) of a candidate design, the contention at the shared cache must be accounted in addition to cycle-by-cycle behavior of the large number of cores which superlinearly increases the number of simulation cycles per iteration of the design exploration. Third, single thread performance does not scale linearly with number of hardware threads per core and number of cores due to memory wall effect. This means that at every step of the design process designers must ensure that single thread performance is not unacceptably slowed down while increasing overall throughput. While all these factors affect design decisions in both high performance and embedded many-core processors, the design of embedded processors required for complex embedded applications such as networking, smart power grids, battlefield decision-making, consumer electronics and biomedical devices to name a few, is fundamentally different from its high performance counterpart because of the need to consider (i) low power and (ii) real-time operations. This implies the design objective for embedded many-core processors cannot be to simply maximize performance, but improve it in such a way that overall power dissipation is minimized and all real-time constraints are met. This necessitates additional power estimation models right at the design stage to accurately measure the cost and reliability of all the candidate designs during the exploration phase. In this dissertation, a statistical machine learning (SML) based design exploration framework is presented which employs an execution-driven cycle- accurate simulator to accurately measure power and performance of embedded many-core processors. The embedded many-core processor domain is Network Processors (NePs) used to processed network IP packets. Future generation NePs required to operate at terabits per second network speeds captures all the aspects of a complex embedded application consisting of shared data structures, large volume of compute-intensive and data-intensive real-time bound tasks and a high level of task (packet) level parallelism. Statistical machine learning (SML) is used to efficiently model performance and power of candidate designs in terms of wide ranges of micro-architectural parameters. The method inherently minimizes number of in-loop simulations in the exploration framework and also efficiently captures the non-linear interactions between the micro-architectural design parameters. To ensure scalability, the design space is partitioned into (i) core-level micro-architectural parameters to optimize single core architectures subject to the real-time constraints and (ii) shared memory level micro- architectural parameters to explore the shared interconnection network and shared cache memory architectures and achieves overall optimality. The cost function of our exploration algorithm is the total power dissipation which is minimized, subject to the constraints of real-time throughput (as determined from the terabit optical network router line-speed) required in IP packet processing embedded application

    Parameter estimation and arrangement optimization of particle dampers on the cantilever rectangular plate

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    The particle damper as a passive means for vibration suppression, is preferred due to its simplicity and easy to implement and control, with no need for any auxiliary power equipment. The bottleneck of the design for the particle damper is that damping performance is influenced by many parameters including particle material and size, packing ratio and cavity dimensions of enclosure such as length, width and diameter. In fact, the overall damping effect of particle damper is closely related to the primary structure system parameters. However, the influences of the excitation point, dampers arrangements, excitation force amplitude, and excitation force type for overall damping effect are scarce reported in the open literatures. How to exert particle damper on the structure and how many particles filled within the cavity, which are needed to face the problem in the engineering practice. In the face of doubt above-mentioned, the above questions will be clarified in this paper. At the same time, an optimization algorithm by the application of genetic algorithms of BP neural network is carried out in order to get most excellent damping effect. These will offer significance guidance to design and conduct particle dampers implement in the specific engineering practice with reference significance

    Design and Investigation of Genetic Algorithmic and Reinforcement Learning Approaches to Wire Crossing Reductions for pNML Devices

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    Perpendicular nanomagnet logic (pNML) is an emerging post-CMOS technology which encodes binary data in the polarization of single-domain nanomagnets and performs operations via fringing field interactions. Currently, there is no complete top-down workflow for pNML. Researchers must instead simultaneously handle place-and-route, timing, and logic minimization by hand. These tasks include multiple NP-Hard subproblems, and the lack of automated tools for solving them for pNML precludes the design of large-scale pNML circuits

    CFD Applications in Energy Engineering Research and Simulation: An Introduction to Published Reviews

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    Computational Fluid Dynamics (CFD) has been firmly established as a fundamental discipline to advancing research on energy engineering. The major progresses achieved during the last two decades both on software modelling capabilities and hardware computing power have resulted in considerable and widespread CFD interest among scientist and engineers. Numerical modelling and simulation developments are increasingly contributing to the current state of the art in many energy engineering aspects, such as power generation, combustion, wind energy, concentrated solar power, hydro power, gas and steam turbines, fuel cells, and many others. This review intends to provide an overview of the CFD applications in energy and thermal engineering, as a presentation and background for the Special Issue “CFD Applications in Energy Engineering Research and Simulation” published by Processes in 2020. A brief introduction to the most significant reviews that have been published on the particular topics is provided. The objective is to provide an overview of the CFD applications in energy and thermal engineering, highlighting the review papers published on the different topics, so that readers can refer to the different review papers for a thorough revision of the state of the art and contributions into the particular field of interest

    Data Mining Technology for Structural Control Systems: Concept, Development, and Comparison

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    Structural control systems are classified into four categories, that is, passive, active, semi-active, and hybrid systems. These systems must be designed in the best way to control harmonic motions imposed to structures. Therefore, a precise powerful computer-based technology is required to increase the damping characteristics of structures. In this direction, data mining has provided numerous solutions to structural damped system problems as an all-inclusive technology due to its computational ability. This chapter provides a broad, yet in-depth, overview in data mining including knowledge view (i.e., concept, functions, and techniques) as well as application view in damped systems, shock absorbers, and harmonic oscillators. To aid the aim, various data mining techniques are classified in three groups, that is, classification-, prediction-, and optimization-based data mining methods, in order to present the development of this technology. According to this categorization, the applications of statistical, machine learning, and artificial intelligence techniques with respect to vibration control system research area are compared. Then, some related examples are detailed in order to indicate the efficiency of data mining algorithms. Last but not least, capabilities and limitations of the most applicable data mining-based methods in structural control systems are presented. To the best of our knowledge, the current research is the first attempt to illustrate the data mining applications in this domain
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