• The Transactions of the Korea Information Processing Society
• /
• v.5 no.11
• /
• pp.2973-2979
• /
• 1998

As this paper is a scheduling algorithm for the synthesis of a pipelined datapath under resource constraints in high level synthesis, the proposed heuristic algorithm uses a priority function based on the collision count of resourecs. In order to schedule the pipelined datapath under resource constraints, we define the collision count and the priority function based on the collision count, a number of resource, and the mobility of operations to resolve a resource collision. The proposed algorithm supports chaining, multicycling, and structural pipelining to design the realistic hardware. The evaluation of the Performance is compared with other systems using the results of the synthesis for a 16point FIR filter and a 5th order elliptic wave filter, where in most cases, the optimal solution is obtained.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.44 no.12
• /
• pp.18-24
• /
• 2007

As VLSI technology scales to nano-meter order, relatively increasing global wire-delay has added complexity to system design. Global wire-delay could be reduced by inserting pipeline-elements onto wire but it should be coupled with LIP(Latency Intensive Protocol) to have correct system timing. This combination however, drops the throughput although it ensures system functionality. In this paper, we propose a computation method useful for minimizing throughput deterioration when pipeline-elements are inserted to reduce global wire-delay. We apply this method while placing blocks in the floorplanning stage. When the necessary for this computation is reflected on the floorplanning cost function, the throughput increases by 16.97% on the average when compared with the floorplanning that uses the conventional heuristic throughput-evaluation-method.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.2
• /
• pp.87-94
• /
• 2004

The feature subset selection is used as a preprocessing step of a teaming algorithm. If collected data are irrelevant or redundant information, we can improve the performance of learning by removing these data before creating of the learning model. The feature subset selection can also reduce the search space and the storage requirement. This paper proposed a new feature subset selection algorithm that is using the heuristic function based on entropy to evaluate the performance of the abstracted feature subset and feature selection. The ACS algorithm was used as a search method. We could decrease a size of learning model and unnecessary calculating time by reducing the dimension of the feature that was used for learning.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.4
• /
• pp.147-152
• /
• 2016

One of major issues in machine learning that extracts and acquires knowledge implicit in data is to find an appropriate way of representing it. Knowledge can be represented by a number of structures such as networks, trees, lists, and rules. The differences among these exist not only in their structures but also in effectiveness of the models for their problem solving capability. In this paper, we propose partition utility as a criterion function for clustering that can lead to simplification of the model and thus avoid overfitting problem. In addition, a heuristic is proposed as a way to construct balanced hierarchical models.

• Journal of KIISE:Computer Systems and Theory
• /
• v.31 no.10
• /
• pp.562-574
• /
• 2004

We address the problem of energy-optimal voltage scheduling for fixed-priority hard real-time systems. First, we prove that the problem is NP-hard. Then, we present a fully polynomial time approximation scheme (FPTAS) for the problem. for any $\varepsilon$>0, the proposed approximation scheme computes a voltage schedule whose energy consumption is at most (1+$\varepsilon$) times that of the optimal voltage schedule. Furthermore, the running time of the proposed approximation scheme is bounded by a polynomial function of the number of input jobs and 1/$\varepsilon$. Experimental results show that the approximation scheme finds more efficient voltage schedules faster than the best existing heuristic.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.42 no.3
• /
• pp.254-262
• /
• 2014

Radar task scheduling deals with the assignment of task to efficiently enhance the radar performance on the limited resource environment. In this paper, total weighted tardiness is adopted as the objective function of task scheduling in operation of multiple multi-function radars. To take into account real-time implementability, heuristic index-based methods are presented and investigated. Numerical simulations for generic search and track scenarios are performed to evaluate the proposed methods, in particular investigating the effectiveness of multi-radar operation concepts.

• Journal of Sensor Science and Technology
• /
• v.6 no.2
• /
• pp.145-154
• /
• 1997

Based on a new search strategy using circuit simulation and simulated annealing with local search, a technique for design automation of high-performance operational amplifiers is proposed. For arbitrary circuit topology and performance specifications, through discrete optimization of a cost function with discrete design variables the design of operational amplifiers is performed. A special-purpose circuit simulator and some heuristics are used to reduce the design time. Through the design of a low-power high-speed fully differential CMOS operational amplifier usable in smart sensors and 10-b 25-MS/s pipelined A/D converters, it has been demonstrated that a design tool developed using the proposed technique can be used for designing high-performance operational amplifiers with less design knowledge and less design effort.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.7
• /
• pp.1715-1724
• /
• 2013

The maximum lifetime data aggregation problem is to maximize the network lifetime as minimizing the transmission energy of all deployed nodes in wireless sensor networks. In this paper, we propose a simulated annealing algorithm to solve efficiently the maximum lifetime data aggregation problem on the basis of meta-heuristic approach in wireless sensor networks. In order to make a search more efficient, we propose a novel neighborhood generating method and a repair function of the proposed algorithm. We compare the performance of the proposed algorithm with other existing algorithms through some experiments in terms of the network lifetime and algorithm computation time. Experimental results show that the proposed algorithm is efficient for the maximum lifetime data aggregation problem in wireless sensor networks.

• Journal of KIISE:Software and Applications
• /
• v.34 no.3
• /
• pp.246-254
• /
• 2007

Local search algorithms start from a certain candidate solution and probe its neighborhood to find ones with improved quality. This paper proposes a method of probabilistically filtering out bad-looking neighbors based on a simple low-cost preliminary evaluation heuristics. The probabilistic filtering enables us to save time wasted on fully evaluating those solutions that will eventually be trashed, and thus improves the search efficiency by allowing us to spend more time on examining better looking solutions. Experiments with two large-scaled real-world problems, which are a traffic signal control problem in traffic network and a load balancing problem in production scheduling, have shown that the proposed method finds better quality solutions, given the same amount of CPU time.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.4B
• /
• pp.236-244
• /
• 2012

The minimum energy broadcast problem is for all deployed nodes to minimize a total transmission energy for performing a broadcast operation in wireless networks. In this paper, we propose a Tabu search algorithm to solve efficiently the minimum energy broadcast problem on the basis of meta-heuristic approach in wireless sensor networks. In order to make a search more efficient, we propose a novel neighborhood generating method and a repair function of the proposed algorithm. We compare the performance of the proposed algorithm with other existing algorithms through some experiments in terms of the total transmission energy of nodes and algorithm computation time. Experimental results show that the proposed algorithm is efficient for the minimum energy broadcast problem in wireless sensor networks.