Brains are the most powerful computers known. Now microchips built to mimic insects' nervous systems have been shown to successfully tackle technical computing problems like object recognition and data mining, researchers say.
Attempts to recreate how the brain works are nothing new. Computing principles underlying how the organ operates have inspired computer programs known as neural networks, which have been used for decades to analyze data. The artificial neurons that make up these programs imitate the brain's neurons, with each one capable of sending, receiving and processing information.
However, real biological neural networks rely on electrical impulses known as spikes. Simulating networks of spiking neurons with software is computationally intensive, setting limits on how long these simulations can run and how large they can get.
To overcome these restraints, several groups around the world have started developing so-called "neuromorphic hardware" that use models of spiking neurons on microchips. For instance, Qualcomm released its Zeroth chip in October 2013. The company advertises the chip as part of its next generation of mobile devices for image and speech processing.
A major advantage that brains have over conventional computers is how they can solve many problems in parallel simultaneously. However, conventional algorithms are often difficult to implement on neuromorphic hardware—novel algorithms that embrace the nature of brain-like computing architecture have to be used instead.
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Published on March 7 of 2014, John Niman, an IEET Affiliate Scholar talks about Neuromorphic Hardware on his youtube channel boydfuturist.
Brains are the most powerful computers known. Now microchips built to mimic insects' nervous systems have been shown to successfully tackle technical computing problems like object recognition and data mining, researchers say.
Attempts to recreate how the brain works are nothing new. Computing principles underlying how the organ operates have inspired computer programs known as neural networks, which have been used for decades to analyze data. The artificial neurons that make up these programs imitate the brain's neurons, with each one capable of sending, receiving and processing information.
However, real biological neural networks rely on electrical impulses known as spikes. Simulating networks of spiking neurons with software is computationally intensive, setting limits on how long these simulations can run and how large they can get.
To overcome these restraints, several groups around the world have started developing so-called "neuromorphic hardware" that use models of spiking neurons on microchips. For instance, Qualcomm released its Zeroth chip in October 2013. The company advertises the chip as part of its next generation of mobile devices for image and speech processing.
A major advantage that brains have over conventional computers is how they can solve many problems in parallel simultaneously. However, conventional algorithms are often difficult to implement on neuromorphic hardware—novel algorithms that embrace the nature of brain-like computing architecture have to be used instead.
Click Here to read more...
https://www.youtube.com/watch?v=FBbDeMcQsu0lis