Cognitive computing is a branch of computing that uses computerized models to find answers to certain complex problems just like a brain would do. Cognitive computing is basically an amalgamation of neuromorphic and Von Neumann model of computing. Traditionally computers were based on the Von Neumann model of computers as they used to perform analytic operations rather than performing reasoning operations however computer networking nowadays has become continuously more and more progressive. The world today is looking at the integration of artificial intelligence and machine learning in computers and various devices to solve various complex issues. In present times many devices use Artificial Neural Networking (ANN) that are used to mimic the logic the brain works on, to complete some very complicated tasks. Although ANN is very helpful yet it has a certain limiting point. Such a scenario has given rise to a new form of computing known as Cognitive Computing.
What is Neuromorphic Computing?
Neuromorphic computing involves designing computer chips that are run on the same physics of computation used by our own brain. These chips use neural networking to communicate with one another. The main goal of neuromorphic computing is to perform very complex logical and reasoning operations in a very short time and simultaneously using very less amount of power. Although neural networking is very efficient but still it cannot completely replace Von Neumann model of computing as the analytic and iterative operations are performed better by it than the neuromorphic system .Such amalgamation is similar to that present in our brain where left brain is performing analytical operations representing the Von Neumann model of computing and right brain performs all operations involving reasoning and creativity analogous to the neuromorphic model.
Architecture of Cognitive computing
Cognitive computing is a kind of heterogeneous model that makes the working of computerized devices wholesome in all aspects making it capable to solve any and every kind of problem that a human brain, as well as computer, could tackle. The architecture of cognitive computing chips has Neurosynaptic cores that operate in parallel as nodes(neurons) that comprise of the processor(cell body), data bus(axon) and memory(synapse). Such nodes have been assigned specific weights and are fed with a large amount of data that ultimately interconnect with each other to perform tasks. These chips keep on analyzing and learning from the data continuously.
Main Features of Cognitive Computing chips are-
- These work in a clockless event-driven fashion leading to a decrease in energy consumption and an increase in performance.
- The clockless event-driven fashion here means that unlike traditional processors where each stage of logic has to be synchronized, in cognitive computers each stage can be asynchronous where components of logic can run at different speeds completing the action more quickly.
- Neuroplasticity: Cognitive computing chips are fault-tolerant and do not stop working if one of the Neurosynaptic core stops working. The neural net self-adapts and routes through other cores just like our brain does.
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