The world’s first supercomputer capable of simulating networks at the scale of the human brain is all set to launch in 2024. DeepSouth – the world’s first supercomputer capable of mimicking the human brain and behavior, was developed by researchers at the International Centre for Neuromorphic Systems (ICNS) at Western Sydney University.
DeepSouth uses a neuromorphic system that mimics biological processes, using advanced and unique hardware to match large networks of spiking neurons at 228 trillion synaptic operations per second, rivaling the estimated rate of operations in the human brain.
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How DeepSouth: The World’s First Supercomputer is Different:
ICNS Director, Professor André van Schaik, says DeepSouth stands apart from other supercomputers as it is purpose-built to operate like networks of neurons, requiring less power and enabling greater efficiencies. This contrasts with supercomputers optimised for more traditional computing loads, which are power-hungry.
“Progress in our understanding of how brains compute using neurons is hampered by our inability to simulate brain-like networks at scale. Simulating spiking neural networks on standard computers using graphics processing units (GPUs) and multicore central processing units (CPUs) is just too slow and power-intensive. Our system will change that,” Professor van Schaik said. Read the official press release.
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“This platform will progress our understanding of the brain and develop brain-scale computing applications in diverse fields, including sensing, biomedical, robotics, space, and large-scale AI applications.”
Professor van Schaik explained that practically, this will lead to advances in smart devices, such as mobile phones, sensors for manufacturing and agriculture, and less power-hungry and smarter AI applications. It will also enable a better understanding of how a healthy or diseased human brain works.
Who made DeepSouth – The World’s First Supercomputer
Western Sydney University’s ICNS team collaborated with partners across the neuromorphic field in developing this ground-breaking project, with researchers from the University of Sydney, the University of Melbourne, and the University of Aachen, Germany.
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Why World’s First Supercomputer named as DeepSouth
The supercomputer is named DeepSouth to pay homage to IBM’s TrueNorth system, which initiated efforts to build machines simulating large networks of spiking neurons, and Deep Blue, the first computer to become a world chess champion. The name is also a nod to its geographical location.
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What is the Launch Date of DeepSouth:
DeepSouth will be based at Western Sydney University and is a key contributor to the growth of the region as a high-tech hub.
DeepSouth aims to be operational by April 2024.
How the World’s First Supercomputer DeepSouth will Benefit:
Super-fast, large-scale parallel processing using far less power: Brains can process the equivalent of an exaflop — a billion-billion (1 followed by 18 zeros) mathematical operations per second — with just 20 watts of power. Using neuromorphic engineering that simulates how our brain works, DeepSouth can process massive amounts of data quickly, using much less power and much smaller than other supercomputers.
Scalability: The system is also scalable, allowing for adding more hardware to create a larger system or scaling down for smaller portable or more cost-effective applications.
Reconfigurable: Leveraging Field Programmable Gate Arrays (FPGA) facilitates hardware reprogramming, enabling the addition of new neuron models, connectivity schemes, and learning rules—overcoming limitations seen in other neuromorphic computing systems with custom-designed hardware.
DeepSouth will be remotely accessible with a front end that allows a description of the neural models and design of the neural networks in the popular programming language Python. The development of this front-end enables researchers to use the platform without needing detailed knowledge of the hardware configuration.
Commercial Availability: Leveraging commercially available hardware ensures continual improvements of the hardware, independent of the team designing the supercomputer, overcoming limitations seen in other neuromorphic computing systems with custom-designed hardware.
Custom chips take a large amount of time to design and manufacture and cost tens of millions of dollars each. Using commercial off-the-shelf configurable hardware means that the prototype would be easy to replicate at data centres around the world.
Artificial Intelligence: By mimicking the brain, it will be easy to create more efficient ways of undertaking AI processes than our current models.
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