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

Sensorless control of DC drive using artificial neural network

DOI nefunkčníThe paper deals with the application of an artificial neural network in the speed control of the DC drive without a speed sensor. The sensorless control structure of the DC drive contains the feedforward artificial neural network for speed estimation. The sensorless DC drive was simulated in program Matlab with Simulink toolbox. The main goal was to find the simplest artificial neural network structure with minimum number of neurons, but simultaneously good control characteristics are required. Despite the used neural network, which is very simple, it was achieved satisfactory results. The simulation results were confirmed by measurement of important quantities on a laboratory stand with the DC drive.Web of Science111020

Implementation of an extended prediction self-adaptive controller using LabVIEW (TM)

The implementation of the Extended Prediction Self-Adaptive Controller is presented in this paper. It employs LabVIEWTM graphical programming of industrial equipment and it is suitable for controlling fast processes. Three different systems are used for implementing the control algorithm. The research regarding the controller design using graphical programming demonstrates that a single advanced control application can run on Windows, real time operating systems and FPGA targets without requiring significant program modifications. The most appropriate device may be selected according to the required processing time of the control signal and of the application. A relevant case study is used to exemplify the procedure

Sigmoid Function Implementation Using the Unequal Segmentation of Differential Lookup Table and Second Order Nonlinear Function

This paper discusses the artificial neural network (ANN) implementation into a field programmable gate array (FPGA). One of the most difficult problem encounters is the complex equation of the activation function namely sigmoid function. The sigmoid function is used as learning function to train the neural network while its derivative is used as a network activation function for specifying the point at which the network should switch to a true state. In order to overcome this problem, two-steps approach which combined the unequal segmentation of the differential look-up table (USdLUT) and the second order nonlinear function (SONF) is proposed. Based on the analysis done, the deviation achieved using the proposed method is 95%. The result obtained is much better than the previous implementation that uses equal segmentation of differential look-up table

Simulink modeling and design of an efficient hardware-constrained FPGA-based PMSM speed controller

The aim of this paper is to present a holistic approach to modeling and FPGA implementation of a permanent magnet synchronous motor (PMSM) speed controller. The whole system is modeled in the Matlab Simulink environment. The controller is then translated to discrete time and remodeled using System Generator blocks, directly synthesizable into FPGA hardware. The algorithm is further refined and factorized to take into account hardware constraints, so as to fit into a low cost FPGA, without significantly increasing the execution time. The resulting controller is then integrated together with sensor interfaces and analysis tools and implemented into an FPGA device. Experimental results validate the controller and verify the design

A Portable Implementation on Industrial Devices of a Predictive Controller Using Graphical Programming

This paper presents an approach for developing an Extended Prediction Self-Adaptive Controller employing graphical programming of industrial standard devices, for controlling fast processes. For comparison purposes, the algorithm has been implemented on three different FPGA (Field Programmable Gate Arrays) chips. The paper presents research aspects regarding graphical programming controller design, showing that a single advanced control application can run on different targets without requiring significant program modifications. Based on the time needed for processing the control signal and on the application, one can efficiently and easily select the most appropriate device. To exemplify the procedure, a conclusive case study is presented

Speed Sensorless Control with ANN and Fuzzy PI Adaptation Mechanism for Induction Motor Drive

In the speed sensorless induction motor drives system, the Rotor Flux based Model Reference Adaptive System (RF-MRAS) is the most common strategy. It suffers from parameter sensitivity and flux pure integration problems. As a result, it leads to the deterioration of speed estimation. Simultaneously, the traditional PI parameters design may cause speed estimation instability or have gross errors in the regenerative mode. To overcome above-mentioned problems, a suitable Artificial Neural Networks (ANN) based on Ant Colony Optimization (ACO) is presented to replace the reference model of the RF-MRAS. Furthermore, the ANN learning by the modified ACO can enhance the ANN convergence speed and avoids the trap of local minimum value of algorithm. In the meantime, a fuzzy PI adaptation mechanism is also put forward, so the proportional coefficient kp and the integral coefficient ki can be adjusted dynamically to adapt the speed variations. Finally, the simulation results suggest that the speed estimation is more accurate in both the dynamic and static process, and the stability of speed estimation in regenerative mode was improved

Multicore and FPGA implementations of emotional-based agent architectures

The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-014-1307-6.Control architectures based on Emotions are becoming promising solutions for the implementation of future robotic agents. The basic controllers of the architecture are the emotional processes that decide which behaviors of the robot must activate to fulfill the objectives. The number of emotional processes increases (hundreds of millions/s) with the complexity level of the application, reducing the processing capacity of the main processor to solve complex problems (millions of decisions in a given instant). However, the potential parallelism of the emotional processes permits their execution in parallel on FPGAs or Multicores, thus enabling slack computing in the main processor to tackle more complex dynamic problems. In this paper, an emotional architecture for mobile robotic agents is presented. The workload of the emotional processes is evaluated. Then, the main processor is extended with FPGA co-processors through Ethernet link. The FPGAs will be in charge of the execution of the emotional processes in parallel. Different Stratix FPGAs are compared to analyze their suitability to cope with the proposed mobile robotic agent applications. The applications are set up taking into account different environmental conditions, robot dynamics and emotional states. Moreover, the applications are run also on Multicore processors to compare their performance in relation to the FPGAs. Experimental results show that Stratix IV FPGA increases the performance in about one order of magnitude over the main processor and solves all the considered problems. Quad-Core increases the performance in 3.64 times, allowing to tackle about 89 % of the considered problems. Quad-Core has a lower cost than a Stratix IV, so more adequate solution but not for the most complex application. Stratix III could be applied to solve problems with around the double of the requirements that the main processor could support. Finally, a Dual-Core provides slightly better performance than stratix III and it is relatively cheaper.This work was supported in part under Spanish Grant PAID/2012/325 of "Programa de Apoyo a la Investigacion y Desarrollo. Proyectos multidisciplinares", Universitat Politecnica de Valencia, Spain.Domínguez Montagud, CP.; Hassan Mohamed, H.; Crespo, A.; Albaladejo Meroño, J. (2015). Multicore and FPGA implementations of emotional-based agent architectures. Journal of Supercomputing. 71(2):479-507. https://doi.org/10.1007/s11227-014-1307-6S479507712Malfaz M, Salichs MA (2010) Using MUDs as an experimental platform for testing a decision making system for self-motivated autonomous agents. Artif Intell Simul Behav J 2(1):21–44Damiano L, Cañamero L (2010) Constructing emotions. Epistemological groundings and applications in robotics for a synthetic approach to emotions. In: Proceedings of international symposium on aI-inspired biology, The Society for the Study of Artificial Intelligence, pp 20–28Hawes N, Wyatt J, Sloman A (2009) Exploring design space for an integrated intelligent system. Knowl Based Syst 22(7):509–515Sloman A (2009) Some requirements for human-like robots: why the recent over-emphasis on embodiment has held up progress. Creat Brain Like Intell 2009:248–277Arkin RC, Ulam P, Wagner AR (2012) Moral decision-making in autonomous systems: enforcement, moral emotions, dignity, trust and deception. In: Proceedings of the IEEE, Mar 2012, vol 100, no 3, pp 571–589iRobot industrial robots website. http://www.irobot.com/gi/ground/ . Accessed 22 Sept 2014Moravec H (2009) Rise of the robots: the future of artificial intelligence. Scientific American, March 2009. http://www.scientificamerican.com/article/rise-of-the-robots/ . Accessed 14 Oct 2014.Thu Bui L, Abbass HA, Barlow M, Bender A (2012) Robustness against the decision-maker’s attitude to risk in problems with conflicting objectives. IEEE Trans Evolut Comput 16(1):1–19Pedrycz W, Song M (2011) Analytic hierarchy process (AHP) in group decision making and its optimization with an allocation of information granularity. IEEE Trans Fuzzy Syst 19(3):527–539Lee-Johnson CP, Carnegie DA (2010) Mobile robot navigation modulated by artificial emotions. IEEE Trans Syst Man Cybern Part B 40(2):469–480Daglarli E, Temeltas H, Yesiloglu M (2009) Behavioral task processing for cognitive robots using artificial emotions. Neurocomputing 72(13):2835–2844Ventura R, Pinto-Ferreira C (2009) Responding efficiently to relevant stimuli using an emotion-based agent architecture. Neurocomputing 72(13):2923–2930Arkin RC, Ulam P, Wagner AR (2012) Moral decision-making in autonomous systems: enforcement, moral emotions, dignity, trust and deception. Proc IEEE 100(3):571–589Salichs MA, Malfaz M (2012) A new approach to modeling emotions and their use on a decision-making system for artificial agents. Affect Comput IEEE Trans 3(1):56–68Altera Corporation (2011) Stratix III device handbook, vol 1–2, version 2.2. http://www.altera.com/literature/lit-stx3.jsp . Accessed 14 Oct 2014.Altera Corporation (2014) Stratix IV device handbook, vol 1–4, version 5.9. http://www.altera.com/literature/lit-stratix-iv.jsp . Accessed 14 Oct 2014.Naouar MW, Monmasson E, Naassani AA, Slama-Belkhodja I, Patin N (2007) FPGA-based current controllers for AC machine drives: a review. IEEE Trans Ind Electr 54(4):1907–1925Intel Corporation (2014) Desktop 4th generation Intel Core Processor Family, Desktop Intel Pentium Processor Family, and Desktop Intel Celeron Processor Family, Datasheet, vol 1, 2March JL, Sahuquillo J, Hassan H, Petit S, Duato J (2011) A new energy-aware dynamic task set partitioning algorithm for soft and hard embedded real-time systems. Comput J 54(8):1282–1294Del Campo I, Basterretxea K, Echanobe J, Bosque G, Doctor F (2012) A system-on-chip development of a neuro-fuzzy embedded agent for ambient-intelligence environments. IEEE Trans Syst Man Cybern Part B 42(2):501–512Pedraza C, Castillo J, Martínez JI, Huerta P, Bosque JL, Cano J (2011) Genetic algorithm for Boolean minimization in an FPGA cluster. J Supercomput 58(2):244–252Orlowska-Kowalska T, Kaminski M (2011) FPGA implementation of the multilayer neural network for the speed estimation of the two-mass drive system. IEEE Trans Ind Inf 7(3):436–445Cassidy AS, Merolla P, Arthur JV, Esser SK, Jackson B, Alvarez-icaza R, Datta P, Sawada J, Wong TM, Feldman V, Amir A, Ben-dayan D, Mcquinn E, Risk WP, Modha DS (2013) Cognitive computing building block: a versatile and efficient digital neuron model for neurosynaptic cores. In: Proceedings of international joint conference on neural networks, IEEE (IJCNN’2013)IBM Cognitive Computing and Neurosynaptic chips website. http://www.research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml . Accessed 22 Sept 2014Seo E, Jeong J, Park S, Lee J (2008) Energy efficient scheduling of real-time tasks on multicore processors. IEEE Trans Parallel Distrib Syst 19(11):1540–1552Lehoczky J, Sha L, Ding Y (1989) The rate monotonic scheduling algorithm: exact characterization and average case behavior. In: Proceedings of real time systems symposium, IEEE 1989, pp 166–171Ng-Thow-Hing V, Lim J, Wormer J, Sarvadevabhatla RK, Rocha C, Fujimura K, Sakagami Y (2008) The memory game: creating a human-robot interactive scenario for ASIMO. In: Proceedings of intelligent robots and systems, 2008, IROS 2008, IEEE/RSJ international conference, pp 779–78

Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function

Artificial neural network (ANN) is an established artificial intelligence technique that is widely used for solving numerous problems such as classification and clustering in various fields. However, the major problem with ANN is a factor of time. ANN takes a longer time to execute a huge number of neurons. In order to overcome this, ANN is implemented into hardware namely field-programmable-gate-array (FPGA). However, implementing the ANN into a field-programmable gate array (FPGA) has led to a new problem related to the sigmoid function implementation. Often used as the activation function for ANN, a sigmoid function cannot be directly implemented in FPGA. Owing to its accuracy, the lookup table (LUT) has always been used to implement the sigmoid function in FPGA. In this case, obtaining the high accuracy of LUT is expensive particularly in terms of its memory requirements in FPGA. Second-order nonlinear function (SONF) is an appealing replacement for LUT due to its small memory requirement. Although there is a trade-off between accuracy and memory size. Taking the advantage of the aforementioned approaches, this thesis proposed a combination of SONF and a modified LUT namely differential lookup table (dLUT). The deviation values between SONF and sigmoid function are used to create the dLUT. SONF is used as the first step to approximate the sigmoid function. Then it is followed by adding or deducting with the value that has been stored in the dLUT as a second step as demonstrated via simulation. This combination has successfully reduced the deviation value. The reduction value is significant as compared to previous implementations such as SONF, and LUT itself. Further simulation has been carried out to evaluate the accuracy of the ANN in detecting the object in an indoor environment by using the proposed method as a sigmoid function. The result has proven that the proposed method has produced the output almost as accurately as software implementation in detecting the target in indoor positioning problems. Therefore, the proposed method can be applied in any field that demands higher processing and high accuracy in sigmoid function outpu