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Neuromorphic computing may be a method of computer engineering during which elements of a computer are modeled after systems within the human brain and systema nervosum. The term refers to the planning of both hardware and computer science major jobs.
Neuromorphic engineers draw from several disciplines -- including computing, biology, mathematics, electronic engineering, and physics -- to make artificial neural systems inspired by biological structures.
There are two overarching goals of neuromorphic computing (sometimes called neuromorphic engineering). the primary is to make a tool that will learn, retain information and even make logical deductions the way a person's brain can -- a cognition machine. The second goal is to accumulate new information -- and maybe prove a rational theory -- about how the human brain works.
How does neuromorphic computing work?
Traditional neural network and machine learning computation are compatible with existing algorithms. it's typically focused on providing either fast computation or low power, often achieving one at the expense of the opposite.
Neuromorphic systems on the opposite hand, achieve both fast computation and low power consumption. they're also:
massively parallel, meaning they will handle many tasks at once;
event-driven, meaning they answer events supported variable environmental conditions and only the parts of the pc in use require power;
high in adaptability and plasticity, meaning they're very flexible;
able to generalize; and
strong and fault-tolerant, meaning it can still produce results after components have failed.
High energy efficiency, fault tolerance, and powerful problem-solving capabilities are all also traits that the brain possesses. for instance, the brain uses roughly 20 watts of power on the average, which is about half that of a typical laptop. it's also extremely fault-tolerant -- the information is stored redundantly (in multiple places), and even relatively serious failures of certain brain areas don't prevent general function. It also can solve novel problems and adapt to new environments very quickly.
Neuromorphic engineers draw from several disciplines -- including computing, biology, mathematics, electronic engineering, and physics -- to make artificial neural systems inspired by biological structures.
There are two overarching goals of neuromorphic computing (sometimes called neuromorphic engineering). the primary is to make a tool that will learn, retain information and even make logical deductions the way a person's brain can -- a cognition machine. The second goal is to accumulate new information -- and maybe prove a rational theory -- about how the human brain works.
How does neuromorphic computing work?
Traditional neural network and machine learning computation are compatible with existing algorithms. it's typically focused on providing either fast computation or low power, often achieving one at the expense of the opposite.
Neuromorphic systems on the opposite hand, achieve both fast computation and low power consumption. they're also:
massively parallel, meaning they will handle many tasks at once;
event-driven, meaning they answer events supported variable environmental conditions and only the parts of the pc in use require power;
high in adaptability and plasticity, meaning they're very flexible;
able to generalize; and
strong and fault-tolerant, meaning it can still produce results after components have failed.
High energy efficiency, fault tolerance, and powerful problem-solving capabilities are all also traits that the brain possesses. for instance, the brain uses roughly 20 watts of power on the average, which is about half that of a typical laptop. it's also extremely fault-tolerant -- the information is stored redundantly (in multiple places), and even relatively serious failures of certain brain areas don't prevent general function. It also can solve novel problems and adapt to new environments very quickly.
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