A peak power optimization scheduling algorithm for single cycle operations and multi-cycle operations

In this paper, we present a combination of basic retiming and multiple voltage scheduling (MVS) techniques in order to optimize dynamic peak power as well as average power consumption in synchronous sequential circuits under timing constraints. First, we devise a mixed-integer linear programming (MILP) formulation for the problem of scheduling for optimal peak and/or average power consumption through a unification of retiming and MVS techniques. Then, to alleviate the problem of variable explosion in MILP, we present a two-stage algorithm for peak and average power optimization. First, power-oriented retiming is proposed to restructure the input SDFG in order to achieve parallelization to favor nodes with high power consumption followed by an MILP formulation for peak and/or average power optimization using MVS technique

A combination of novel piezoelectric inkjet print head technologies such as greyscale, shared wall and genuine throughflow have led to the development of inkjet print heads able to deliver the quality and reliability demanded by modern single-pass printing applications. While shared wall technology can provide benefits such as acoustic firing and reduced drive voltage, there are limitations in the final achievable firing frequency, dictated by a 3-cycle firing pattern (active-idle-idle). As a result the print speed is restricted.Recent advances in the exploitation of the base shared wall technology have allowed the development of single cycle nozzle operation (always active) for shared wall devices. This has yielded approximately a three-fold increase in firing frequency and hence the three-fold increase in print speed from a print head of similar footprint. Such dramatic improvement has been possible by deeper understanding of the events involved in the complete drop ejection cycle combined with clever rearrangement and overlapping of the events for arrays of nozzles working in synchronization.Three implementations of a single cycle operation have been explored, some which impose imaging limitations and others which use technically complex solutions but imaging capabilities are unhindered. Commercialization of these technology variants is currently underway with some already deployed in end user production environments.

In the current design of automatic storage warehouse,the proportion of single and compound cycle operation is approximately estimated based on experience according to even acceleration of SRM machines, which leads to the discrepancy between planning and design ability and actual system ability. To solve this problem, we analyzed the actual running condition of SRM machine based on SRM kinematics theory. According to the speed and time curve of SRM machines, the time calculation formula of SRM machine on the direction of X, Y&Z axis and mathematical model of SRM machine capability calculation were proposed. According to the scheduling management strategy, the random probability of actual SRM machine performance, and the working flow of SRM machine as well, are analyzed. The mathematical model and algorithm was set up on automatic logistic tiered warehouse storage system capacity based on random probability. Precision convergence method and evaluation process of capacity calculation were given as well. The discrepancy between planning and design ability and actual system ability is solved. Finally, by means of the finished product warehouse of certain cigarette factory, calculation result of estimating algorithm and random probability algorithm were compared with the actual measurement data respectively, which proved that result of random probability algorithm was closer to actual running status and it was more feasible and practicable. Automatic tiered warehouse storage system is a key subsystem of automated logistics system, which is mainly composed of stacker and shelves, thus completing the material automatic access and storage in the tiered warehouse, and the key technical indicators is accessibility. In the current system planning and design, generally the logistics standard approximate method is adopted to estimate the system capacity. The proportion of single cycle operation and compound cycle operation is made approximate estimation based on experience, and the acceleration of stacker is made approximate treatment by constant acceleration. Since the actual working state of the system is not considered, there is difference between the computing capacity and actual capacity. After the project is completed, the actual capacity of automated logistics system cannot meet the design requirements, thus affecting the production and use of the enterprise, which have occurred from time to time. Therefore, it is supposed to study on the calculating method of the system capacity when making planning and design, which is one of the affecting factors - so

Power monitoring system for university buildings: Architecture and advanced analysis tools

The design principles that were applied in the design of the integer-RISC machine, are applied to the design of the Reduced Instruction Set Graphics Processor. The processor instruction is defined after critical evaluation of the common graphics operations. Single cycle operation (in a pure RISC style) are performed through instructions for primitive graphics as well as general purpose computations. This processor is envisioned to become the CPU of choice for the ergonomic applications that rely heavily on graphics capabilities for user interface bandwidth improvement. The motivation for integrating graphics with general-purpose computations stems from the performance enhancement acheived due to increased integration levels, and the program portability offered by a single processor based systems.

Granular carbon dioxide sorbent preparation for cyclic operations

With advancing technology, large dynamic power consumption has significantly limited circuit miniaturization. Minimizing peak power consumption, which is defined as the maximum power consumption among all voltage partitions, is important since it enables energy saving from the voltage island shutdown mechanism. In this paper, we prove that the peak power driven voltage partitioning problem is NP-complete and propose an efficient provably good fully polynomial time approximation scheme for it. The new algorithm can approximate the optimal peak power driven voltage partitioning solution in O(m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (mn/ϵ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> )) time within a factor of (1 + ϵ) for sufficiently small positive e, where n is the number of circuit blocks and m is the number of partitions which is a small constant in practice. Our experimental results demonstrate that the dynamic programming cannot finish for even 20 blocks while our new approximation algorithm runs fast. In particular, varying e, orders of magnitude speedup can be obtained with only 0.6% power increase. The tradeoff between the peak power minimization and the total power minimization is also investigated. We demonstrate that the total power minimization algorithm obtains good results in total power but with quite large peak power, while our peak power optimization algorithm can achieve on average 26.5% reduction in peak power with only 0.46% increase in total power. Moreover, our peak power driven voltage partitioning algorithm is integrated into a simulated annealing based floorplanning technique. Experimental results demonstrate that compared to total power driven floorplanning, the peak power driven floorplanning can significantly reduce peak power with only little impact in total power, HPWL, estimated power ground routing cost, level shifter cost and runtime. Further, when the voltage island shutdown is performed, peak power driven voltage partitioning can lead to over 10% more energy saving than a greedy frequency based voltage partitioning when multiple idle block sequences are considered.

The paper reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including Firefly algorithm, PSO algorithms and ABC algorithm. By implementing these algorithms in Matlab, we will use worked examples to show how each algorithm works. Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by the flashing behaviour of fireflies in nature. There are many noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. Mathematical optimization or programming is the study of such planning and design problems using mathematical tools. Nowadays, computer simulations become an indispensable tool for solving such optimization problems with various efficient search algorithms. Nature-inspired algorithms are among the most powerful algorithms for optimization. Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behaviour of fireflies. A Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, which simulates the flash pattern and characteristics of fireflies. It is a powerful swarm intelligence algorithm inspired by the flash phenomenon of the fireflies. In this context, three types of meta-heuristics called Artificial bee Colony algorithm, Particle Swarm Optimization (PSO) and Firefly algorithms were devised to find optimal solutions of noisy non-linear continuous mathematical models. A series of computational experiments using each algorithm were conducted. The stimulation result of this experiment were analyzed and compared to the best solutions found so The Firefly algorithm in each noisy non linear optimization function seems to perform better and efficient.

Long-wavelength vertical-cavity surface-emitting lasers (LW-VCSELs) have profound advantages over the traditional edge-emitting lasers offering improved properties with respect to mode selectivity, fiber coupling, threshold currents and integration into 2D arrays or with other electronic devices. Its commercialization is gaining momentum as the local and access network in optical communication system expands. This paper reports the optimization of the peak lasing power and peak lasing wavelength of a LW-VCSEL using a numerical-based simulator and Taguchi's orthogonal array methodology. Initially, peak lasing power increment of 96.5% was achieved at 9.51 mW with peak wavelength of 1.55956 μm. Next, we attempted to bring the peak wavelength on target to 1.55 μm. It was found that a reduction of DBR mirror thicknesses by 1.3% from its nominal values is able to produce a device with lasing powers of 11.62 mW and on-target peak wavelength of 1.55 μm.

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.

A novel control strategy for DVR: Optimal bi-objective structure emotional learning

Age of Information (AoI) is an emerging concept to model information freshness from the perspective of destinations of information deliveries. Serving as a metric to characterize the data delivery timeliness, the peak age indicates the maximum value of the AoI prior to a data packet reception. In this paper, we present a distributed scheduling algorithm for peak age optimization in a wireless network where a number of sensor nodes attempt to deliver their sensed data to a data collector over a wireless channel. In particular, each sensor node accesses the channel for data delivery independently according to an adaptively tuned transmission probability. The beauty of our algorithm lies in that, even with neither centralized infrastructure nor coordinations among the sensor nodes, our algorithm asymptotically approximates the optimal solution by only a constant factor. We perform solid theoretical analysis and extensive simulations to verify the efficacy of our algorithm. To the best of our knowledge, it is the first fully distributed scheduling algorithm for AoI optimization in wireless networks.

In Static List Scheduling (SLS), initially the ALAP (As Late As Possible) algorithm is performed, followed by ASAP (As Soon As Possible) algorithm. The time interval for control steps and successor selection are the vital drawback of ASAP algorithm. Hence, in this paper, the optimized scheduling algorithm is proposed for High Level Synthesis (HLS), where the ALAP in SLS is followed by Force Directed Scheduling (FDS). The proposed scheduling algorithm is implemented in Icarus Verilog and targeted in ZC7020 FPGA board. The proposed optimized scheduling algorithm is evaluated in terms of control steps and resource utilization. The evaluation is carried out for various benchmarks such as, EW Filter, IIR filter, FIR filter, AR filter and Discrete Cosine Transform (DCT). The achieved results in terms of the control steps and resource utilization (i.e. LUT and Flip-flop) shows the proposed optimized scheduling algorithm outperforms the conventional Static List Scheduling (SLS) algorithm.

Optimal scheduling for energy harvesting mobile sensing devices

For an efficient energy harvesting by the PV/thermoelectric system, the maximum power point tracking (MPPT) principle is targeted, aiming to operate the system close to peak power point. Under a uniform distribution of the solar irradiance, there is only one maximum power point (MPP), which easily can be efficiently determined by any traditional MPPT method, such as the incremental conductance (INC). A different situation will occur for the non-uniform distribution of solar irradiance, where more than one MPP will exist on the power versus voltage plot of the PV/thermoelectric system. The determination of the global MPP cannot be achieved by conventional methods. To deal with this issue the application of soft computing techniques based on optimization algorithms is used. However, MPPT based on optimization algorithms is very tedious and time consuming, especially under normal conditions. To solve this dilemma, this research examines a hybrid MPPT method, consisting of an incremental conductance (INC) approach and a moth-flame optimizer (MFO), referred to as (INC-MFO) procedure, to reach high adaptability at different environmental conditions. In this way, the combination of the two different algorithms facilitates the utilization of the advantages of the two methods, thereby resulting in a faster speed tracking with uniform radiation distribution and a high accuracy in the case of a non-uniform distribution. It is very important to mention that the INC method is used to track the maximum power point under normal conditions, whereas the MFO optimizer is most relevant for the global search under partial shading. The obtained results revealed that the proposed strategy performed best in both of the dynamic and the steady-state conditions at uniform and non-uniform radiation.