Engineering Seminar Topics and Seminar Topics

Download Full Article Neural Networks and Their Applications

1.1 What is a Neural Network?

An Artificial Neural Network (ANN) is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. 

1.2 Historical background

Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived several eras. Many important advances have been boosted by the use of inexpensive computer emulations. The first artificial neuron was produced in 1943 by the neurophysiologist Warren McCulloch and the logician Walter Pitts.

1. First Attempts: There were some initial simulations using formal logic. McCulloch and Pitts (1943) developed models of neural networks based on their understanding of neurology. These models made several assumptions about how neurons worked. Their networks were based on simple neurons, which were considered to be binary devices with fixed threshold.

2. Promising & Emerging Technology: Not only was neuroscience, but psychologists and engineers also contributed to the progress of neural network simulations. Rosenblatt (1958) stirred considerable interest and activity in the field when he designed and developed the Perceptron. The Perceptron had three layers with the middle layer known as the association layer. This system could learn to connect or associate a given input to a random output unit.

Another system was the ADALINE (Adaptive Linear Element) which was developed in 1960 by Widrow and Hoff (of Stanford University). The ADALINE was an analogue electronic device made from simple components. The method used for learning was different to that of the Perceptron, it employed the Least-Mean-Squares (LMS) learning rule.

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Download Full Article PARALLEL COMPUTING IN INDIA

            Although the performance of single processors has been steadily increasing over the years, the only way to build the next generation teraflop architecture supercomputers seems to be through parallel processing technology. Even with today’s workstation-class high performance processors exceeding 100 megaflops, thousands of processors are required to build a teraflop architecture machine. Further, the fastest special purpose vector processors have a few Gigaflop peak performance, and thus they too need to be utilized in parallel to achieve Teraflop levels of performance.

            In 1987, India decided to launch a national initiative in supercomputing in the form of a time-bound mission to design, develop and deliver a supercomputer in the gigaflops range. The major motivation came from delays (political) in getting a CRAY XMP for weather forecasting. A decision was made to support the development of indigenous parallel processing technology. The Center for Development of Advanced Computing (C-DAC) was set up in August 1988 with 3-year budget of Rs. 375 million (approximately US$ 12 million).

         C-DAC’s First Mission was directed to deliver 1000 MFlops parallel supercomputer (1GF) by 1991. Simultaneously, several other complementary projects were initiated to develop high-performance parallel computers at the National Aerospace Laboratory of the Council of Scientific and Industrial Research (CSIR), the Center for Development of Telematics (C-DOT), Advanced Numerical Research & Analysis Group (ANURAG) of Defense Research and Development Organization (DRDO) and Bhabha Atomic Research Center (BARC). India’s first generation parallel computers were delivered starting from 1991.

PARALLEL PROCESSING

              We all know that the silicon based chips are reaching a physical limit in processing speed, as they are constrained by the speed of electricity, light and certain thermodynamic laws. A viable solution to overcome this limitation is to connect multiple processors working in coordination with each other to solve grand challenge problems. Hence, high performance computing requires the use of Massively Parallel Processing (MPP) systems containing thousands of power full CPUs.

        Processing of multiple tasks simultaneously on multiple processors is called Parallel Processing. The parallel program consists of multiple active processes simultaneously solving a given problem. A given task is divided into multiple sub tasks using divide-and-conquer technique and each one of them are processed on different CPUs. Programming on multiprocessor system using divide-and-conquer technique is called Parallel Processing.

The development of parallel processing is being influenced by many factors. The prominent among them include the following:

Ø  Computational requirements are ever increasing, both in the area of scientific and business computing. The technical computing problems, which require high-speed computational power, are related to life sciences, aerospace, geographical information systems, mechanical design and analysis, etc.

Ø  Sequential architectures reaching physical limitation, as they are constrained by the speed of light and thermodynamics laws. Speed with which sequential CPUs can operate is reaching saturation point ( no more vertical growth ), and hence an alternative way to get high computational speed is to connect multiple CPUs ( opportunity for horizontal growth ).

Ø  Hardware improvements in pipelining, super scalar, etc, are non scalable and requires sophisticated compiler technology. Developing such compiler technology is difficult task.

Ø  Vector processing works well for certain kind of problems. It is suitable for only scientific problems ( involving lots of matrix operations). It is not useful to other areas such as database.

Ø  The technology of parallel processing is mature and can be exploited commercially, there is already significant research and development work on development tools and environment is achieved.

Ø  Significant development in networking technology is paving a way for heterogeneous computing.

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