Computational Intelligence
M.R. Esmaeili; S.H. Zahiri; S.M. Razavi
Abstract
Background and Objectives: High-level synthesis (HLS) is one of the substantial steps in designing VLSI digital circuits. The primary purpose of HLS is to minimize the digital units used in the system to improve their power, delay, and area.Methods: In the modified MFO algorithm presented in this paper, ...
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Background and Objectives: High-level synthesis (HLS) is one of the substantial steps in designing VLSI digital circuits. The primary purpose of HLS is to minimize the digital units used in the system to improve their power, delay, and area.Methods: In the modified MFO algorithm presented in this paper, a hyperbolic spiral is chosen as the update mechanism of moths. Also, by presenting a new approach, a paramount issue involved in applying meta-heuristic methods for solving HLS problems of VLSI circuits has been disentangled.Results: By comparing the performance of the proposed method with Genetic algorithm (GA)-based method and particle swarm optimization (PSO)-based method for the synthesis of the digital filters, it is concluded that the proposed method has the higher ability in the HLS of data path in digital filters. The best improvement is 2.78% for the delay (latency), 6.51% for the occupied area of the chip and 6.93% in power consumption. Another feature of the proposed method is its high-speed in finding optimal solutions, in a manner which, more than 21.6% and 12.9% faster than the GA-based and PSO-based methods, respectively on average.Conclusion: The most important very large scale integration (VLSI) circuits are digital filters and transformers, which are widely used in audio and video processing, medical signal processing, and telecommunication systems. The complex, expansive, and discrete nature of design space in high-level synthesis problems has made them one of the most difficult problems in VLSI circuit design.
Modelling, simulation and verification
S.M. Nematollahzadeh; S. Ozgoli; M. Sayad Haghighi
Abstract
Background and Objectives: One of the interesting topics in the field of social networks engineering is opinion change dynamics in a discussion group and how to use real experimental data in order to identify an interaction pattern among individuals. In this paper, we propose a method that utilizes experimental ...
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Background and Objectives: One of the interesting topics in the field of social networks engineering is opinion change dynamics in a discussion group and how to use real experimental data in order to identify an interaction pattern among individuals. In this paper, we propose a method that utilizes experimental data in order to identify the influence network between individuals in social networks.Methods: The employed method is based on convex optimization and can identify interaction patterns precisely. This technique considers individuals’ opinions in multiple dimensions. Moreover, the opinion dynamics models that have been introduced in the literature are investigated. Then, the three models which are the most comprehensive and vastly accepted in the literature, are considered. These three models are then proven to satisfy the convexity condition, which means they can be used for the introduced method of identification.Results: Four real experiments have been conducted in this research that their results verify the application of our method. The outcomes of these experiments are presented in this paper.Conclusion: Results show that the provide method is suited for parameter identification for opinion dynamic models.
