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Hardware Technology

SpiNNaker Powers Up World's Largest Supercomputer That Emulates a Human Brain 164

Posted by BeauHD from the up-and-running dept.
The world's largest neuromorphic supercomputer, the Spiking Neural Network Architecture (SpiNNaker), was just switched on for the first time yesterday, boasting one million processor cores and the ability to perform 200 trillion actions per second. HotHardware reports: SpiNNaker has been twenty years and nearly $19.5 million in the making. The project was originally supported by the Engineering and Physical Sciences Research Council (EPSRC), but has been most recently funded by the European Human Brain Project. The supercomputer was designed and built by the University of Manchester's School of Computer Science. Construction began in 2006 and the supercomputer was finally turned on yesterday.

SpiNNaker is not the first supercomputer to incorporate one million processor cores, but it is still incredibly unique since it is designed to mimic the human brain. Most computers send information from one point to another through a standard network. SpiNNaker sends small bits of information to thousands of points, similar to how the neurons pass chemicals and electrical signals through the brain. SpiNNaker uses electronic circuits to imitate neurons. SpiNNaker has so far been used to mimic the processing of more isolated brain networks like the cortex. It has also been used to control SpOmnibot, a robot that processes visual information and navigates towards its targets.
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SpiNNaker Powers Up World's Largest Supercomputer That Emulates a Human Brain More

SpiNNaker Powers Up World's Largest Supercomputer That Emulates a Human Brain

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  • Suprising... (Score:5, Interesting)

    by Anonymous Coward on Monday November 05, 2018 @02:20AM (#57592378)

    ...that the article does not mention that the project lead, Steve Furber was one of the team at Acorn that created the original ARM chip back in the 80s.

  • "switched on for the first time" vs "has been used (Score:2, Interesting)

    by Anonymous Coward

    Which is it, genius editors ?

    • Re: (Score:1)

      by Anonymous Coward
      The editors have not used it yet. I mean their own....

    • Because between the "yesterday" referenced in the article when they "turned it on for the first time" and the "now" when the article was written there wasn't enough time to use the machine in any form or function, right? It's just sitting there...idle...like a giant fucking paperweight and does absolutely fucking nothing. Or maybe there might actually be enough time in the 12 - 24 hrs that differentiates "yesterday" from "today" that they could have spun up some simulations that they had ready to go.

      BTW..

  • Really, if it could *really* emulate the human brain and they switched it on yesterday, it should have done *something* by now that was worth reporting...

    And with only million cpu's, isn't that a few orders of magnitude to small to emulate a human brain anyways, which has hundreds of billions of neurons?

    • > only million cpu's, isn't that a few orders of magnitude to small to emulate a human brain anyways, which has hundreds of billions of neurons?

      Yup, this "simulation" is off by an order of magnitudes.

      The brain is estimated to have 86 Billion Neurons; the number of connections even higher.

      Trying to use inorganic matter to simulate consciousness is a fools errand. They should start with bio-organic computing instead -- they would have better luck.

      • Re:All hype, no content (Score:5, Insightful)

        by ShanghaiBill ( 739463 ) on Monday November 05, 2018 @03:10AM (#57592488)

        > only million cpu's, isn't that a few orders of magnitude to small to emulate a human brain anyways, which has hundreds of billions of neurons?

        No. There is no reason that you need 1 cpu per neuron.

        A biological neuron fires 200 times per second. A single core can simulate the firing of 10,000 neurons per second.

        Also, there is no reason the simulation needs to be real-time, so the speed isn't really that important.

        • And in a years' time, SpiNNaker will have asked, very slowly, "Why... was... I... named... after... part... of... a... boat?"

        • You may have a point, they may be able to simulate the firing of 10K, but I'll give you something else to explain. We have no idea how or why neurons take in their various inputs, weigh them and send impulses to a select few of their outputs. Exactly how does the programming account for that?

      • Re: (Score:2)

        by q_e_t ( 5104099 )
        You don't need to have a 1:1 mapping of neurons to CPU cores. A CPU core is capable of emulating multiple neurons, in fact thousands of them, as the activation rules of neurons are not individually particularly complex. Simplisticly, the complexity is in being able to efficiently map connections between neurons, the effect of neurotransmitters and other support structures on the functioning, different types of brain area, and being able to make it function efficiently as a whole. There are plenty of papers

        • Re: (Score:2)

          by q_e_t ( 5104099 )
          P.S. I am not sure how many neurons/CPU SpiNNaker is emulating, or exactly what activation functions it emulates, but the activation functions are not normally considered to be that complex, hence being able to get more than one neuron/CPU. However, any one of those neurons could have connections to any other neuron. Normally with parallel programming you aim to pack a subdomain of the problem onto a CPU, so as to minimise communication, to make it efficient. How SpiNNaker solves the rather more general pro

          • but the activation functions are not normally considered to be that complex

            Cite please.

            • Re: (Score:2)

              by q_e_t ( 5104099 )

              Traditionally, the activation function was f(W) where W is the set of incoming neuron activations over the synaptic connections, such that a=f(W) where a is an activation level, then the neuron fires if a>A, but now it's more common to use spiking neurons, i.e. a time-series element such that, say, a neuron that's had some pattern of events come in, then fires. That's what SpiNNaker uses, see https://en.wikipedia.org/wiki/... [wikipedia.org] is an initial bit of reading, which includes "SpiNNaker (Spiking Neural Networ

              • The dendrites perform complex non-linear computations prior to forwarding signals to the main cell body (achieved via local/regional spiking forward and backward throughout the dendrites) that scientists are just now discovering and trying to figure out.

                In addition, glial cells manage the synapse activity (see tripartite synapse) and regional groups of neuron, you can't ignore that functionality.

      • Re:All hype, no content (Score:5, Insightful)

        by religionofpeas ( 4511805 ) on Monday November 05, 2018 @03:36AM (#57592554)

        Trying to use inorganic matter to simulate consciousness is a fools errand.

        Why ? Unless you can point out some fundamental limitations, it's nothing but an argument from incredulity. It's like saying we can only make a functional wing from feathers, and not aluminum.

        • Trying to use inorganic matter to simulate consciousness is a fools errand.

          Why ? Unless you can point out some fundamental limitations, it's nothing but an argument from incredulity. It's like saying we can only make a functional wing from feathers, and not aluminum.

          You keep saying that ... and AI keeps being just around the corner ...

          • Re: (Score:2)

            by Layzej ( 1976930 )

            You keep saying that ... and AI keeps being just around the corner ...

            Have you been living under a rock? AI is used in everything from fraud detection, natural language processing, self driving cars, customer service, customer retention, automated detection and classification, etc, etc, etc.

            • Have you been living under a rock? AI is used in everything from fraud detection, natural language processing, self driving cars, customer service, customer retention, automated detection and classification, etc, etc, etc.

              All of those cases are narrow/weak AI and not general purpose/strong AI. It's actually the distinction to help people understand we don't have working self driving cars today - the number of fringe edge cases and level of abstraction needed is beyond our current abilities. General purpose AI has an innate human/animal level common sense notion of the world and is self aware.

              I always thought the chinese room/Turing argument against strong AI didn't frame the question properly. Following a set of operat

              • Consciousness is likely similar in that is the emergent behavior once a critical number of rooms with the right contents are connected.

                Consciousness is not like a light that can be on or off. It's more like a bag of different tools and tricks. You can have a few of them, and you'd have a limited form of consciousness, or you can have a large bag of human-like tools, and have a human level consciousness. For AI applications, it is most often not desirable to give the machine a large bag with tools it doesn't need. There's no need for a self driving car to be curious or get bored, for example.

                • I'd agree, it not binary yes/no but rather a spectrum as real life applications often are. Unfortunately for self driving cars it's not so simple as lane following or constant distance car following. Edge cases make it difficult to have a narrow AI back end learn it all, but rather you would need seperate processes for each edge case class and an executive function moderate them and seamlessly fuse behavior with core driving algorithms. Basically a way to do it would be have a variety of narrow AI modules

            • Gloried Table Lookup is NOT Artificial Intelligence; more like Artificial Ignorance.

              • Re: (Score:3)

                by Layzej ( 1976930 )

                "Intelligence is whatever machines haven't done yet.”

                If and when artificial intelligence surpasses human intelligence, people might conclude that there is no such thing as intelligence. Or they might simply redefine intelligence as "whatever humans haven't done yet” as they try to catch up with AI. - Tesler

        • It is obvious you aren't a programmer. You aren't thinking about it from a programmer's debugging perspective. Would you rather:

          * Start from a simpler base that ALREADY works (such as an Earthworm) and trying to figure out how the pieces work, or

          * Start from complexity literally billions of order complicated and TRY to debug that???

          Just to put the connections into perspective:

          * Each neuron may be connected to up to 10,000 other neurons,
          * The minimum total number of connections is estimated to be 100+ trill

          • No, that analogy is flawed. When reverse engineering you ALWAYS start with something that ALREADY works.

            The analogy was about the requirement to have brains built out of organic matter. You're talking about something else. Next time, try to address the actual argument.

            Trying to use a Linear process to understand a Non-Linear system will never work.

            That's why all neural models are non-linear.

            Without a way to MEASURE it, HOW do you know if what you are doing is moving towards or away from the goal post???

            Quite simple. You just focus on the behavior. You can measure the inputs and outputs, and if they get closer to the behavior of a real brain, you know you're getting closer to the goal post. That's how evolution shaped our brain after all, simply by looking at the outputs and see if they benefit survi

          • Mind != Brain. There have been dozens of experiments showing the Non-Locality of Mind..

            Citations please...

            • Go read all of Karl Pribram's research/books or C.J.S. Clarke's Explaining Consciousness: The Hard Problem, and thanatology examples such as Ring and Valarino 1998; Sabom 1982 and 1998, etc.

              Also of interest will be Jonathan Shear's Explaining Consciousness: The Hard Problem, David Chalmers's Toward a Scientific Basis of Consciousness, and David Bohm's work.

      • Re: (Score:1)

        by kmoser ( 1469707 )

        > only million cpu's, isn't that a few orders of magnitude to small to emulate a human brain anyways, which has hundreds of billions of neurons?

        Yup, this "simulation" is off by an order of magnitudes.

        The brain is estimated to have 86 Billion Neurons; the number of connections even higher.

        So, then it's a simulation of several hundred Congressman's brains?

      • They should start small, say a rat brain. There are plenty of rats to take apart and figure out how their brain works, and it's cheaper to work at a smaller scale. We assume most of the underlying principles are the same (we don't actually know how brains work), so there is no disadvantage of starting small. Once they have a working (silicon) rat brain they can scale it up to a useful level (replacing human workers, then discovering that the unemployed don't buy stuff but can still vote to nationalize th

        • Itâ(TM)s been done actually. I canâ(TM)t rmemeber whether it was a rat or a mouse. Also, a cockroach and at least one kind of worm.

        • They should start small, say a rat brain.

          Neuroscientists are currenty trying to get a grip on the nervours system of c. elegans, a tiny worm with a grand total of 300 neurons.

          They have a long way to go, as that even after mapping those 300 neurons six years ago,
          the scientists involved have gained only a very limited understanding of what those neurons actually do.

          At this rate, understanding a mouse brain is decades away.

    • Not really. It'll be simulating a few neurons in the brain at one millionth speed. These things are for medical research, not AI.

    • Re:All hype, no content (Score:5, Insightful)

      by Henriok ( 6762 ) on Monday November 05, 2018 @02:37AM (#57592426)
      A human brain rarely does something spectacular the first days of being turned on.

      • ... which babies can be quite good at!

      • This. I always laugh at videos of newly born animals falling over trying to take their first steps. And then cry a bit when I realise the years it takes us the dominant species to achieve the same feat.

    • Obligatory Skynet (Score:5, Funny)

      by mfnickster ( 182520 ) on Monday November 05, 2018 @07:30AM (#57592974)

      "Skynet becomes self-aware at 02:14 am Eastern Time after its activation on Nov. 4, 2018 and immediately begins shitposting on 4chan."

  • With a Touring Test
    If you have a piece of software that can pass a touring test what have you really created and what does it say about the nature of intelligence ?

    With this
    It would seem that it just validates (not a small thing) the knowledge of how an animal brain is put together, and only in very limited ways at that.

    Overall I suspect this project will tell us much more about what we don't know about how brains are put together than what we do know about how thought works.

  • In all honesty, I doubt it'll go much beyond the 250,000 neuron mark. Brain simulators tend to also be very slow, the ones I could find on Google could take a few hours to simulate a second of activity.

    Based on the core count versus simulation speed versus neurons, a simulator that could handle the whole brain at one second per second would be five miles in diameter and 1,500 feet high.

    That doesn't mean this simulator is unimportant. Simulating fractions of the brain in extended time will let neurologists see the effects of medical interventions. That, and not HAL, is the objective of such projects, after all.

    • Re: (Score:2, Offtopic)

      by q_e_t ( 5104099 )
      Are you assuming one neuron per core? You can have a neural network with many, many units running on a single CPU core. In that case the number of units in in the hundreds to thousands, typically, which means that neuron-to-neuron communication is relatively easy to handle as you can simply use shared memory. The trick with SpiNNaker and similar efforts is being able to marshall communication with more diverse connections and communication, and that gets complex when communications are not within the locali

      • Re: (Score:2)

        by jd ( 1658 )

        The more neurons you have on a core, the less processing time you have per neuron since you're running them as time-shared rather than concurrent.

        The main problem is in the synapses. Up to 3000 per neuron, self-modifying not only in terms of end-points but also in terms of amplifying signals. If you've done network simulation, you'll know that's going to eat into the clock cycles.

        https://www.telegraph.co.uk/te... [telegraph.co.uk]

        40 minutes to simulate one second is not good. So if you want to run the simulation faster, you

    • First, scientists need to understand what one single neuron does to be able to simulate. Current understanding is that a single neuron is actually more like an entire neural network due to all of the non-linear calcs performed by the 10,000 dendrites (not just sum, it's more complex with local/regional spiking, forward and backward, all kinds of stuff).

      Secondly, scientists will need to figure out what exactly glial cells are doing, they control the synapse and manage the action, detecting and releasing n
  • If anyone else is wondering how they can afford a million cores with a budget of less than 20 million dollars: Wikipedia says they are some ARM cores in 10 19 inch racks.
    Each core is supposed to be able to simulate 1000 neurons.
  • Sure, this will definitely help us understand the brain and probably AI in general, but don't mistake it for an "electronic brain" on the level of a humans as some people are doing, either for fun or in ignorance.

    Also, don't forget that our wetware already has a lot of programming in it by default, and we don't even pretend to be able to read and comprehend that source code. So even if we did do a good duplicate of the brain, it's just not going to be the same.
    A further complication that may invalidate a lo

    • Some work in the past couple of years has identified structures that might make each of those cells in our brains be the equivalent of many quibits in a quantum processor!

      The brain is too warm and noisy to exploit quantum coherence on any meaningful scale.

      • Re: (Score:1)

        by lurcher ( 88082 )

        Plants are warm as well.
        http://www.bbc.co.uk/earth/story/20160715-organisms-might-be-quantum-machines

        • Plants are warm as well.

          https://physicsworld.com/a/is-... [physicsworld.com]

          So then, is photosynthesis “quantum” or not? “The observations show that there is correlation between the wavefunctions of the states involved in energy or electron transfer,” says Romero. “But these effects are not considered by some scientists as truly quantum coherence in the sense that entangled states of quantum computing are understood.” And Engel agrees that to compare the two is to invoke “the wrong language”.

  • Yeah, but can it play doom?

  • Its now known that the white matter in the brain isn't passive after all and does affect information processing, plus the neurons are also affected by "out of band" (for want of a better term) signals in the form of hormones. So unless you simulate all of that then at best it'll be a brain-lite even if they simulated all 100 billion neurons (which I very much doubt).

    • According to the article, this machine also simulates chemical signalling.

      • Cells are not connected like electrical circuitry. They don't touch but send a chemical from one dendrite to another. That's why they're so much slower than a cpu.
    • New info has the axon containing the memory. IOW, a single CPU should be accessing a portion of the memory, as opposed to all CPUs being able to access all memory ( though slowly ).
    • It's a good point. Scientists now understand that the synapse (new term is tripartite synapse) is managed and controlled by the glial cell surrounding the synapse. A network of neurons without the functionality of the glial cells managing the activity in the synapses and the flow of information will not really behave correctly.
  • Understand the words you're trying to use:

    emulate VERB

    computing
    reproduce the function or action of (a different computer, software system, etc.).

    simulate VERB

    imitate the appearance or character of.
    produce a computer model of.

    They are not interchangeable.
  • To truely emulate the brain, then memories have to be in each CPU. They have found that neurons now contain our memories, so a true setup would be like teradata DB, than a Von Neumann computer architecture.

    • To truely emulate the brain, then memories have to be in each CPU.

      What makes you think the CPUs don't have local memory to store the various parts of state of the neuron ?

  • We have no idea how our brains really work, and this toy they built at best could only 'emulate' perhaps an insects' brain.

    • We have no idea how our brains really work

      That's why they build machines like this. Trying out the parts that you think you understand is a good way to discover what's still missing. And I'm sure that the scientists who build the actual neural models understand the challenges better than you do.

      this toy they built at best could only 'emulate' perhaps an insects' brain.

      The article doesn't say how big it is right now, but the are planning to extend it to 1 billion neurons. That's bigger than a cat's brain.

  • Heard this on NPR a few days ago by someone on the team. They said a mouse brain cause its close to a human brain.
    hey anyone know where I can get some really good cheese?

  • Is it really "incredibly unique"? Damn, that so much more unique than almost every other unique thing! I mean, most unique things are unique, but this thing is apparently incredibly more so.

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