Artificial neural network model for arrival time computation in gate level circuits

Advances in the VLSI process technology lead to variations in the process parameters. These process variations severely affect the delay computation of a digital circuit. Under such variations, the various delays, i.e. net delay, gate delay, etc., are no longer deterministic. They are random in nature and are assumed to be probabilistic. They keep changing, based on factors such as process, voltage, temperature, and a few others. This calls for efficient tools to perform timing checks on a design. This work presents a technique to compute the arrival time of a digital circuit. The arrival time (AT) is computed using two different timing engines, namely, static timing analysis (STA) and statistical static timing analysis (SSTA). This work also aims to eliminate number of false paths. It uses a fast and efficient filtering method by utilizing ATPG stuck-at faults and path delay faults. ISCAS-89 benchmark circuits are used for implementation. The results obtained using the probabilistic approach are more accurate than the conventional STA. It has been verified with an Artificial Neural Network (ANN) model. The arrival time calculated using SSTA shows 7% improvement over that of STA. The absolute error is reduced twofold in the case of the ANN model for SSTA

Performance comparison of modified elephant herding optimization tuned MPPT for PV based solar energy systems

Purpose The solar photovoltaic (PV) system is one of the outstanding, clean and green energy options available for electrical power generation. The varying meteorological operating conditions impose various challenges in extracting maximum available power from the solar PV system. The drawbacks of conventional and evolutionary algorithms-based maximum power point tracking (MPPT) approaches are its inability to extract maximum power during partial shading conditions and quickly changing irradiations. Hence, the purpose of this paper is to propose a modified elephant herding optimization (MEHO) based MPPT approach to track global maximum power point (GMPP) proficiently during dynamic and steady state operations within less time. Design/methodology/approach A MEHO-based MPPT approach is proposed in this paper by incorporating Gaussian mutation (GM) in the original elephant herding optimization (EHO) to enhance the optimizing capability of determining the optimal value of DC–DC converter’s duty cycle (D) to operate at GMPP. Findings The effectiveness of the proposed system is compared with EHO based MPPT, Firefly Algorithm (FA) MPPT and particle swarm optimization (PSO) MPPT during uniform irradiation condition (UIC) and partial shading situation (PSS) using simulation results. An experimental setup has been designed and implemented. Simulation results obtained are validated through experimental results which prove the viability of the proposed technique for an efficient green energy solution. Originality/value With the proposed MEHO MPPT, it has been noted that the settling period is lowered by 3.1 times in comparison of FA MPPT, 1.86 times when compared to PSO based MPPT and 1.29 times when compared to EHO based MPPT with augmented efficiency of 99.27%. </jats:sec