{"id":20275,"date":"2023-12-16T10:25:14","date_gmt":"2023-12-16T10:25:14","guid":{"rendered":"https:\/\/redbigfoot.com\/?p=20275"},"modified":"2024-04-03T11:27:55","modified_gmt":"2024-04-03T11:27:55","slug":"a-new-supercomputer-aims-to-closely-mimic-the-human-brain-it-could-help-unlock-the-secrets-of-the-mind-and-advance-ai","status":"publish","type":"post","link":"https:\/\/redbigfoot.com\/a-new-supercomputer-aims-to-closely-mimic-the-human-brain-it-could-help-unlock-the-secrets-of-the-mind-and-advance-ai\/","title":{"rendered":"Supercomputer Mimicking a Brain to Unlock AI Secrets"},"content":{"rendered":"\t\t
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A supercomputer scheduled to go online in April 2024 will rival the estimated rate of operations in the human brain,\u00a0according to researchers in Australia<\/a>. The machine, called DeepSouth, is capable of performing 228 trillion operations per second.<\/p>\n

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It\u2019s the world\u2019s first supercomputer capable of simulating networks of neurons and synapses (key biological structures that make up our nervous system) at the scale of the human brain.<\/p>\n

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DeepSouth belongs to an approach\u00a0known as neuromorphic computing<\/a>, which aims to mimic the biological processes of the human brain. It will be run from the International Centre for Neuromorphic Systems at Western Sydney University.<\/p>\n

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Our brain is the most amazing computing machine we know. By distributing its computing power to billions of small units (neurons) that interact through trillions of connections (synapses), the brain can rival the most powerful supercomputers in the world, while requiring only the same power used by a fridge lamp bulb.<\/p>\n

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Supercomputers, meanwhile, generally take up lots of space and need large amounts of electrical power to run. The world\u2019s most powerful supercomputer, the\u00a0Hewlett Packard Enterprise Frontier<\/a>, can perform just over one quintillion operations per second. It covers 680 square meters (7,300 sq ft) and requires 22.7 megawatts (MW) to run.<\/p>\n

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Our brains can perform the same number of operations per second with just 20 watts of power, while weighing just 1.3kg-1.4kg. Among other things, neuromorphic computing aims to unlock the secrets of this amazing efficiency.<\/p>\n

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Transistors at the limits<\/h2>\n

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On June 30 1945, the mathematician and physicist\u00a0John von Neumann<\/a>\u00a0described the design of a new machine, the\u00a0Electronic Discrete Variable Automatic Computer (Edvac)<\/a>. This effectively defined the modern electronic computer as we know it.<\/p>\n

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My smartphone, the laptop I am using to write this article and the most powerful supercomputer in the world all share the same fundamental structure introduced by von Neumann almost 80 years ago.\u00a0These all have distinct processing and memory units<\/a>, where data and instructions are stored in the memory and computed by a processor.<\/p>\n

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\"DeepSouth.\"<\/figure>\n

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For decades, the number of transistors on a microchip doubled approximately every two years,\u00a0an observation known as Moore\u2019s Law<\/a>. This allowed us to have smaller and cheaper computers.<\/p>\n

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However, transistor sizes are now approaching the atomic scale. At these tiny sizes, excessive heat generation is a problem, as is a phenomenon called quantum tunnelling, which interferes with the functioning of the transistors.\u00a0This is slowing down<\/a>\u00a0and will eventually halt transistor miniaturization.<\/p>\n

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To overcome this issue, scientists are exploring new approaches to computing, starting from the powerful computer we all have hidden in our heads, the human brain. Our brains do not work according to John von Neumann\u2019s model of the computer. They don\u2019t have separate computing and memory areas.<\/p>\n

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They instead work by connecting billions of nerve cells that communicate information in the form of electrical impulses. Information can be passed from\u00a0one neuron to the next through a junction called a synapse<\/a>. The organization of neurons and synapses in the brain is flexible, scalable and efficient.<\/p>\n

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So in the brain \u2013 and unlike in a computer \u2013 memory and computation are governed by the same neurons and synapses. Since the late 1980s, scientists have been studying this model with the intention of importing it to computing.<\/p>\n

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\"Microchip.\"<\/figure>\n

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Imitation of life<\/h2>\n

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Neuromorphic computers are based on intricate networks of simple, elementary processors (which act like the brain\u2019s neurons and synapses). The main advantage of this is that these machines\u00a0are inherently \u201cparallel\u201d<\/a>.<\/p>\n

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This means that,\u00a0as with neurons and synapses<\/a>, virtually all the processors in a computer can potentially be operating simultaneously, communicating in tandem.<\/p>\n

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In addition, because the computations performed by individual neurons and synapses are very simple compared with traditional computers, the energy consumption is orders of magnitude smaller. Although neurons are sometimes thought of as processing units, and synapses as memory units, they contribute to both processing and storage. In other words, data is already located where the computation requires it.<\/p>\n

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This speeds up the brain\u2019s computing in general because there is no separation between memory and processor, which in classical (von Neumann) machines causes a slowdown. But it also avoids the need to perform a specific task of accessing data from a main memory component, as happens in conventional computing systems and consumes a considerable amount of energy.<\/p>\n

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The principles we have just described are the main inspiration for DeepSouth. This is not the only neuromorphic system currently active. It is worth mentioning the\u00a0Human Brain Project (HBP)<\/a>, funded under an\u00a0EU initiative<\/a>. The HBP was operational from 2013 to 2023, and led to BrainScaleS, a machine located in Heidelberg, in Germany, that emulates the way that neurons and synapses work.<\/p>\n

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BrainScaleS<\/a>\u00a0can simulate the way that neurons \u201cspike\u201d, the way that an electrical impulse travels along a neuron in our brains. This would make BrainScaleS an ideal candidate to investigate the mechanics of cognitive processes and, in future, mechanisms underlying serious neurological and neurodegenerative diseases.<\/p>\n

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Because they are engineered to mimic actual brains, neuromorphic computers could be the beginning of a turning point. Offering sustainable and affordable computing power and allowing researchers to evaluate models of neurological systems, they are an ideal platform for a range of applications. They have the potential to both advance our understanding of the brain and offer new approaches to artificial intelligence.<\/p>\n

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Source:\u00a0https:\/\/theconversation.com\/a-new-supercomputer-aims-to-closely-mimic-the-human-brain-it-could-help-unlock-the-secrets-of-the-mind-and-advance-ai-220044<\/a><\/p>\n

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A supercomputer scheduled to go online in April 2024 will rival the estimated rate of operations in the human brain,\u00a0according to researchers in Australia. The machine, called DeepSouth, is capable of performing 228 trillion operations per second. It\u2019s the world\u2019s first supercomputer capable of simulating networks of neurons and synapses (key biological structures that make […]<\/p>\n","protected":false},"author":6,"featured_media":20276,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[61],"yoast_head":"\nSupercomputer Mimicking a Brain to Unlock AI Secrets<\/title>\n<meta name=\"description\" content=\"A supercomputer scheduled to go online in April 2024 will rival the estimated rate of operations in the human brain\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/redbigfoot.com\/a-new-supercomputer-aims-to-closely-mimic-the-human-brain-it-could-help-unlock-the-secrets-of-the-mind-and-advance-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" 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