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ABSTRACT :
Most of the students of Electronics Engineering are exposed to Integrated Circuits (IC's) at a very basic level, involving SSI (small scale integration) circuits like logic gates or MSI (medium scale integration) circuits like multiplexers, parity encoders etc. But there is a lot bigger world out there involving miniaturization at levels so great, that a micrometer and a microsecond are
literally considered huge! This is the world of VLSI - Very Large Scale Integration.
Neural networks are a new method of programming computers. They are exceptionally good at performing pattern recognition and other tasks that are very difficult to program using conventional techniques. Programs that employ neural nets are also capable of learning on their own and adapting to changing conditions.
Neural nets may be the future of computing .A good way to understand them is with a puzzle that neural nets can be used to solve. Suppose that you are given 500 characters of code that you know to be C, C++, Java, or Python. Now, construct a program that identifies the code's language. One solution is to construct a neural net that learns to identify these languages.
According to a simplified account, the human brain consists of about ten billion neurons -- and a neuron is, on average, connected to several thousand other neurons. By way of these connections, neurons both send and receive varying quantities of energy. One very important feature of neurons is that they don't react immediately to the reception of energy.
Instead, they sum their received energies, and they send their own quantities of energy to other neurons only when this sum has reached a certain critical threshold. The brain learns by adjusting the number and strength of these connections. The brain's network of neurons forms a massively parallel information processing system. This contrasts with conventional computers, in which a single processor executes a single series of instructions.
INTRODUCTION:
VLSI stands for "Very Large Scale Integration". This is the field which involves packing more and more logic devices into smaller and smaller areas .Thanks to VLSI, circuits that would have taken broadfulls of space can now be put into a small space few millimeters across! This has opened up a big opportunity to do things that were not possible before. VLSI circuits are everywhere ... your computer, your car, your brand new state-of-the-art digital camera, the cell -phone, etc.
MOST OF TODAY’S VLSI DESIGNS ARE CLASSIFIED INTO THREE CATEOGRIES:
1. Analog 2.Application Specific Integrated Circuits 3.Systems on a chip
The VLSI is also used for Neural Networks in many ways and applications. A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain.
The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. Traditional linear models are simply inadequate when it comes to modeling data that contains non-linear characteristics.
The most common neural network model is the multilayer perceptron (MLP). This type of neural network is known as a supervised network because it requires a desired output in order to learn. The goal of this type of network is to create a model that correctly maps the input to the output using historical data so that the model can then be used to produce the output when the desired output is unknown.
ARTIFICIAL NEURAL NETWORK:
An artificial neural network (ANN) or commonly as Neural Network (NN) is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network.
In more practical terms neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.
A biological neural network is a plexus of connected or functionally related neurons in the peripheral nervous system or the central nervous system. In the field of neuroscience, it most often refers to a group of neurons from nervous systems that are suited for laboratory analysis.
Biological Neural Network
From "Texture of the Nervous System of Man and the Vertebrates". The figure illustrates the diversity of neuronal morphologies in the auditory cortex.
In neuroscience, a neural network is a bit of conceptual juggernaut: the conceptual transition from neuroanatomy, a rigorously descriptive discipline of observed structure, to the designation of the parameters delimiting a 'network' can be problematic. In outline a neural network describes a population of physically interconnected neurons or a group of disparate neurons whose inputs or signaling targets define a recognizable circuit. Communication between neurons often involves an electrochemical process. The interface through which they interact with surrounding neurons usually consists of several dendrites (input connections), which are connected via synapses to other neurons, and one axon (output connection). If the sum of the input signals surpasses a certain threshold, the neuron sends an action potential (AP) at the axon hillock and transmits this electrical signal along the axon.
In contrast, a neuronal circuit is a functional entity of interconnected neurons that influence each other (similar to a control loop in cybernetics).
The neural network is divided into three different categories
DIGITAL:
The digital neural network category encompasses many sub-categories including slice architectures, SIMD and systolic array devices, and RBF architectures. For the designer, digital technology has the advantages of mature fabrication techniques, weight storage in RAM, and arithmetic operations exact within the number of bits of the operands and accumulators. From the users viewpoint, digital chips are easily embedded into most applications. However, digital operations are usually slower than in analog systems, especially in the multiplication, and analog inputs must first be converted to digital.
Multi-processor Chip: A far more elaborate approach is to put many small processors on a chip. Two architectures dominate such designs: single instruction with multiple data (SIMD) and systolic arrays. For SIMD design, each processor executes the same instruction in parallel but on different data. In systolic arrays, a processor does one step of a calculation (always the same step) before passing it's result on to the next processor in a pipelined manner. SIMD chips include the Inova N64000 and the HNC 100 NAP. All chips execute the same instruction and common control and data bases allow for multiple chips to be combined.
Radial Basis Functions: RBF networks provide fast learning and straight-forward interpretation. The comparison of input vectors to stored training vectors can be calculated easily without using multiplication operations. Two commercial RBF products are now available: the IBM ZISC036 (Zero Instruction Set Computer) chip and the Nestor Ni1000 chip. The ZISC036 contains 36 prototype-vector neurons, where the vectors have 64 8-bit elements, and can be assigned to categories from 1 to 16383. Multiple chips can be easily cascaded to provide additional prototypes. The chip implements a Region of Influence learning algorithm using signum basis functions with radii of 0 to 16383. Recall processing takes for a 250k/sec pattern presentation rate. The
Nestor Ni1000, developed jointly by Intel and Nestor, contains 1024 prototypes of 256 5-bit elements. The chip has two on-chip learning algorithms, RCE[21] and PNN[22], and other algorithms can be micro coded. The processing rate is about 40k patterns/sec with a 40MHz clock