Our brains are remarkably energy efficient.
Using just 20 watts of power, the human brain is capable of processing the equivalent of an exaflop — or a billion-billion mathematical operations per second.
Now, researchers in Australia are building what will be the world’s first supercomputer that can simulate networks at this scale.
ICYMI: DeepSouth uses a #neuromorphiccomputing system which mimics biological processes, using hardware to efficiently emulate large networks of spiking #neurons at 228 trillion #Synaptic operations per second — rivalling the estimated rate of operations in the human brain.
Australian researchers are putting together a supercomputer designed to emulate the world’s most efficient learning machine – a neuromorphic monster capable of the same estimated 228 trillion synaptic operations per second that human brains handle.
As the age of AI dawns upon us, it’s clear that this wild technological leap is one of the most significant in the planet’s history, and will very soon be deeply embedded in every part of our lives. But it all relies on absolutely gargantuan amounts of computing power. Indeed, on current trends, the AI servers NVIDIA sells alone will likely be consuming more energy annually than many small countries. In a world desperately trying to decarbonize, that kind of energy load is a massive drag.
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A neuromorphic supercomputer called DeepSouth will be capable of 228 trillion synaptic operations per second, which is on par with the estimated number of operations in the human brain.
Simulations of binary neutron star mergers suggest that future detectors will distinguish between different models of hot nuclear matter.
Researchers used supercomputer simulations to explore how neutron star mergers affect gravitational waves, finding a key relationship with the remnant’s temperature. This study aids future advancements in detecting and understanding hot nuclear matter.
Exploring neutron star mergers and gravitational waves.
The mini-brain functioned like both the central processing unit and memory storage of a supercomputer. It received input in the form of electrical zaps and outputted its calculations through neural activity, which was subsequently decoded by an AI tool.
When trained on soundbites from a pool of people—transformed into electrical zaps—Brainoware eventually learned to pick out the “sounds” of specific people. In another test, the system successfully tackled a complex math problem that’s challenging for AI.
The system’s ability to learn stemmed from changes to neural network connections in the mini-brain—which is similar to how our brains learn every day. Although just a first step, Brainoware paves the way for increasingly sophisticated hybrid biocomputers that could lower energy costs and speed up computation.
Australians develop a supercomputer capable of simulating networks at the scale of the human brain.
The world’s first supercomputer capable of simulating networks at the scale of the human brain has been announced by researchers at Western Sydney University.
Researchers at Western Sydney University in Australia have teamed up with tech giants Intel and Dell to build a massive supercomputer intended to simulate neural networks at the scale of the human brain.
They say the computer, dubbed DeepSouth, is capable of emulating networks of spiking neurons at a mind-melting 228 trillion synaptic operations per second, putting it on par with the estimated rate at which the human brain completes operations.
The project was announced at this week’s NeuroEng Workshop hosted by Western Sydney’s International Centre for Neuromorphic Systems (ICNS), a forum for luminaries in the field of computational neuroscience.
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In the realm of computing technology, there is nothing quite as powerful and complex as the human brain. With its 86 billion neurons and up to a quadrillion synapses, the brain has unparalleled capabilities for processing information. Unlike traditional computing devices with physically separated units, the brain’s efficiency lies in its ability to serve as both a processor and memory device. Recognizing the potential of harnessing the brain’s power, researchers have been striving to create more brain-like computing systems.
Efforts to mimic the brain’s activity in artificial systems have been ongoing, but progress has been limited. Even one of the most powerful supercomputers in the world, Riken’s K Computer, struggled to simulate just a fraction of the brain’s activity. With its 82,944 processors and a petabyte of main memory, it took 40 minutes to simulate just one second of the activity of 1.73 billion neurons connected by 10.4 trillion synapses. This represented only one to two percent of the brain’s capacity.
Continue reading “Scientists built a Cyborg computer with living brain tissue” »
Tesla is pushing the boundaries of AI and supercomputing with the development of Dojo 2, aiming to build the world’s biggest supercomputer by the end of next year, and setting high goals for performance and cost efficiency.
Questions to inspire discussion.
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