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What is neuromorphic computing? - Dataconomy


Neuromorphic computing is a growing computer engineering approach that models and develops computing devices inspired by the human brain. Neuromorphic engineering focuses on using biology-inspired algorithms to design semiconductor chips that will behave similarly to a brain neuron and then work in this new architecture. Neuromorphic computing adds abilities to think creatively, recognize things they've never seen, and react accordingly to machines. Unlike AIs, the human brain is fascinating at understanding cause and effect and adapts to changes swiftly. However, even the slightest change in their environment renders AI models trained with traditional machine learning methods inoperable.

Intel rolls out second-gen Loihi neuromorphic chip with big results in optimization problems


Loihi 2, pronounced "Low-EE-he," cuts in half the size of the chip and multiplies eight-fold the number of artificial spiking neurons. Here, the exterior of the chip is seen with its contacts to connect to the circuit board. Intel on Thursday unveiled the second version of its Loihi neuromorphic chip, "Loihi 2," a processor for artificial intelligence that it claims more aptly reflects the processes that occur in the human brain compared to other AI technology. The new chip is shrunk in half in a more-advanced process node, now measuring 31 square millimeters, yet it contains one million artificial spiking neurons, eight times as many as its predecessor. Loihi, pronounced "low-EE-he," is named for a Hawaiian sea mount, a young volcano, "that is emerging from the sea anytime now," as Mike Davies, Intel's director of neuromorphic computing, puts it.

Intel unveils second-generation neuromorphic computing chip


The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Intel today announced a major update to its neuromorphic computing program, including a second-generation chip called Loihi 2 and Lava, an open-source framework for developing "neuro-inspired" applications. The company is now offering two Loihi 2-based neuromorphic systems -- Oheo Gulch and Kapoho Point -- through a cloud service to members of the Intel Neuromorphic Research Community (INRC) and Lava via GitHub for free. Along with Intel, researchers at IBM, HP, MIT, Purdue, and Stanford hope to leverage neuromorphic computing -- circuits that mimic the human nervous system's biology -- to develop supercomputers 1,000 times more powerful than any today. Custom-designed neuromorphic chips excel at constraint satisfaction problems, which require evaluating a large number of potential solutions to identify the one or few that satisfy specific constraints.

One Step Closer to Deep Learning on Neuromorphic Hardware


A group of researchers at Sandia National Laboratories have developed a tool that can cross-train standard convolutional neural networks (CNN) to a spiking neural model that can be used on neuromorphic processors. The researchers claim that the conversion will enable deep learning applications to take advantage of the much better energy efficiency of neuromorphic hardware, which are designed to mimic the way the biological neurons work. The tool, known as Whetstone, works by adjusting artificial neuron behavior during the training phase to only activate when it reaches an appropriate threshold. As a result, neuron activation become a binary choice – either it spikes or it doesn't. By doing so, Whetstone converts an artificial neural network into a spiking neural network.