Artificial intelligence in Neuromorphic Computing

1. Artificial intelligence in Neuromorphic Computing

2. Neuromorphic computing • A technique for computer engineering known as "neuromorphic computing" uses computer components that are models of the human nervous system and brain. This system is designed for both hardware and software. The term 'morphic' is designed in 1990 by carver mead which refers mixed-signal analog/digital very large integration computer system. Neuromorphic computing has two main objectives 1. Create a device that can learn, retain information, and even make logical deductions from the human brain. 2. Acquire new information and perhaps prove a rational theory about the human brain working.

3. How Neuromorphic Computing Work? • Neuromorphic computing enables the device to make artificial neural networks with millions of artificial neurons. Enable a computer to act and work like human brains. These signals convert input into output. The passing on of electric signal functions based on spiking neural networks. The spiking neural network architecture permits the artificial machine to work like the human brain and perform daily functions. It involves visual recognition, interpretation of data, and a lot more such tasks.

4. Advantage of Neuromorphic computing • It evaluate biological system in real time. • Attempt to replicate the computational power of brain effectively. • Efficient implementations compute in their memory elements more efficient than directly reading all the coefficients. • Precise systems out of imprecise parts.

6. Artificial intelligence development role • As per research paper neuromorphic chips able to read artificial intelligence task such as cognitive computing, adaptive AI,sensing data, artificial intelligence and associate memory. For the right information of sensing or computational data the artificial intelligence development require to identify the given information is recognizable or not.

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