Survey paper on Advanced Equipment Execution of ANN for FPGA

Artificial intelligence is the area of computer science that aims at to create the intelligence machine. Artificial neural network is network that has different processing element. This survey paper recommends the implementation of Artificial Neural Network (ANN) in Field Programmable Gate Array (FPGA) and activates it through sigmoid function. This paper also proposes the implementation of new sigmoid function method in FPGA that combines the Look-Up Table (LUT) and Second Order Nonlinear Function (SONF). By this proposed method ANN works speedily, uses less resource and achieves high accuracy. Keywords: ANN, FPGA, sigmoid function, look up table, second order nonlinear function

A comparative investigation of non-linear activation functions in neural controllers for search-based game AI engineering

The creation of intelligent video game controllers has recently become one of the greatest challenges in game artificial intelligence research, and it is arguably one of the fastest-growing areas in game design and development. The learning process, a very important feature of intelligent methods, is the result of an intelligent game controller to determine and control the game objects behaviors’ or actions autonomously. Our approach is to use a more efficient learning model in the form of artificial neural networks for training the controllers. We propose a Hill-Climbing Neural Network (HillClimbNet) that controls the movement of the Ms. Pac-man agent to travel around the maze, gobble all of the pills and escape from the ghosts in the maze. HillClimbNet combines the hill-climbing strategy with a simple, feed-forward artificial neural network architecture. The aim of this study is to analyse the performance of various activation functions for the purpose of generating neural-based controllers to play a video game. Each non-linear activation function is applied identically for all the nodes in the network, namely log-sigmoid, logarithmic, hyperbolic tangent-sigmoid and Gaussian. In general, the results shows an optimum configuration is achieved by using log-sigmoid, while Gaussian is the worst activation function

A Machine Learning Based Approach to Accelerate Catalyst Discovery

Computational catalysis, in contrast to experimental catalysis, uses approximations such as density functional theory (DFT) to compute properties of reaction intermediates. But DFT calculations for a large number of surface species on variety of active site models are resource intensive. In this work, we are building a machine learning based predictive framework for adsorption energies of intermediate species, which can reduce the computational overhead significantly. Our work includes the study and development of appropriate machine learning models and effective fingerprints or descriptors to predict energies accurately for different scenarios. Furthermore, Bayesian inverse problem, that integrates experimental catalysis with its computational counterpart, uses Markov chain Monte Carlo (MCMC) methods to refine the uncertainties on the quantities-of-interest such as turnover frequency. However, large number of forward simulations required by MCMC can become a bottleneck, especially in computational catalysis, where the evaluation of likelihood functions involves finding the solution to microkinetic models. A novel and faster MCMC method is proposed to reduce the number of expensive target evaluations and to shorten the burn-in period by emulating the target along with using a better informed proposal distribution