নিউরাল নেটওয়ার্ক

নিউরাল নেটওয়ার্ক,মানুষের মস্তিষ্ক গঠিত হয়েছে লক্ষ্ লক্ষ্ নিউরন দিয়ে,এই নিউরন গুলো একে অপরের সাথে জালের মতো জড়িয়ে থাকে,নিউরাল নেটওয়ার্ক এর মাধ্যমে গাণিতিক সমীকরণ দিয়ে বাপারটি explain করা হয়

Artificial Neural Network

Theory:

An Artificial Neural Network is a network of many very simple processors ("units"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data. The units operate only on their local data and on the inputs they receive via the connections.

The design motivation is what distinguishes neural networks from other mathematical techniques: A neural network is a processing device, either an algorithm, or actual hardware, whose design was motivated by the design and functioning of human brains and components thereof.

Fig 02 :Layer specifications in a complex neural network

 Neural Networks are an information processing technique based on the way biological nervous systems, such as the brain, process information. The fundamental concept of neural networks is the structure of the information processing system. Composed of a large number of highly interconnected processing elements or neurons, a neural network system uses the human-like technique of learning by example to resolve problems. The neural network is configured for a specific application, such as data classification or pattern recognition, through a learning process called training. Just as in biological systems, learning involves adjustments to the synaptic connections that exist between the neurons. Neural networks can differ on: the way their neurons are connected; the specific kinds of computations their neurons do; the way they transmit patterns of activity throughout the network; and the way they learn including their learning rate. Neural networks are being applied to an increasing large number of real world problems.

Fig 03: Base neural net object

Their primary advantage is that they can solve problems that are too complex for conventional technologies -- problems that do not have an algorithmic solution or for which an algorithmic solution is too complex to be defined. In general, neural networks are well suited to problems that people are good at solving, but for which computers generally are not. These problems include pattern recognition and forecasting -- which requires the recognition of trends in data. For further information, see the following references:

Fig 04: VLSI chip fabrication Method

System speed is not only dependant on the Processor/Micro-controller but also on the Bus speed the system operates. Contention Prevention mechanism's across multiple drivers. Bus Splitters: Hierarchical Bus structures, based on the speed targets {for example the AMBA bus from ARM, has two bus hierarchy levels : Advanced High Performance Bus[AHB] & Advanced Peripheral Bus[APB]. Split bus architectures has energy efficient transactions and concurrent data transactions over the conventional buses. Reducing latency and crossbar utilization mechanisms. Optimum Bridging Mechanisms for cross data Transfer's among the Buses. Performance Enhancers by having Pipeline mechanism's and steps to prevent Stalling. Arbitration Protocol schemes for shared buses {Fixed Priority Schemes, Round Robin Scheme, Time Division Multiplexing Schemes}.Mechanisms to reduce bus waiting time.Synchronization mechanism's across the bus.Scheduling based on power-profiling. Traffic based Dynamic Voltage and frequency scaling techniques for meeting power-targets.