Silicon Brain: 100,000 ARM cores [video]
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I wonder how it looks at this level of integration (850 cores per board) versus something like Xeon + PHI/GPU. You certainly do gain asynchronous mode of operation, and that might be the winning factor/secret sauce.
Could just look at the website instead of speculating http://apt.cs.manchester.ac.uk/projects/SpiNNaker/SpiNNchip/
All Phis are along the lines of modern cores stripped down and simplified. This is also how the modern Avoton and newer Atoms work, but the Phi is stripped down even farther.
> So why not use an older, smaller but still very capable core? And that is what they did. The designers went back generations, literally back to one of the first modern cores, the Intel® Pentium® processor. [1]
> The foundation for the Intel® Xeon Phi™ coprocessor core PMU is the PMU from the original Intel® Pentium® processor (aka P54C). Most of the forty - two performance events that were available in the original Intel® Pentium® processor are also available on the Intel® Xeon Phi™ coprocessor The core PMU has been upgraded to an Intel® Pentium® Pro processor - like (“P6 - style”) programming interface.
[1] https://software.intel.com/en-us/blogs/2013/03/22/the-intel-...
[2] https://software.intel.com/sites/default/files/forum/278102/... (section 1.2)
This is an extremely small part of the CPU, and yes, I can imagine they jettisoned a lot of stuff you find on modern cores because it takes up too much room.
> The cores at the heart of Intel’s first Xeon Phi are based on the P54C revision of the original Pentium and appear largely unchanged from the design Intel planned to use for Larrabee. [3]
> Many changes were made to the original 32-bit P54c architecture to make it into an Intel Xeon Phi 64-bit processor. [4]
- so, still, they seemed to have started with Pentium I and adding stuff to it rather than stripping out a modern core. Which was always the story they sold about Larrabee, which AFAIK was the direct predecessor project that got salvaged with Intel MIC.
[3] http://www.extremetech.com/extreme/133541-intels-64-core-cha...
[4] https://software.intel.com/en-us/articles/intel-xeon-phi-cor...
What people don't understand is, Intel does the same. Look at how modern E3s, E5s, E7s, i3/5/7s, modern Atoms, etc, all work: similar designed parts, all paired with what is minimally required for that design to work and perform the way they want.
Intel doesn't throw designs out, they keep them and periodically make sure they still work on smaller fab sizes and newer fab techs.
A more striking example than the Phi is the Intel Quark, featured in the Edison platform, which is Intel's equivalent of an ARM Cortex-M series (such as the M4s used in a lot of cell phones as a GPS/motion sub-processor and other things). The Quark really is a modernized P54C (Pentium 1 pre-MMX) core, and more so than the Xeon Phi is (although, obviously, there is shared part design through both of them).
I think the thing with Phi is, its rapidly evolving. Larrabee was closer to this design than first gen Phi was, and now they're shipping second gen Phi, and it looks more like how some GPUs have been historically designed than just x86 core spam (look at how the bus design is evolving, they're getting closer and closer to how AMD and Nvidia design theirs, and also how post-Skylake on-die GPU integration is evolving on multi-socket platforms).
So, yeah. I don't agree that the Phi can be flat out called a P54C, but I agree they have been reusing modernized parts from that era because it is easier to do that than continually strip down existing designs to look like that.
The Quark, however, looks a lot like how embedded family 286 and 486s have been kept alive for the embedded hardware sector, and now they're positioning the Quark for the IoT era (which, hey, they have my interest with that product, so they did something right); the Quark is more of a P54C than the Phi is.
https://en.m.wikipedia.org/wiki/Allen_Institute_for_Brain_Sc...
Interesting. So in terms of raw computing power, the totality of computers out there is much more powerful than a brain. I wonder when we first passed that level as a whole. (I know that we wouldn't use all our computers to make a "brain", but I think it's interesting to think about :)
Instead of SETI@home [0], SAI@home (search for artificial intelligence) ?
[0] setiathome.ssl.berkeley.edu/
In the human brain, there are almost 100 billion neurons (the project plans to simulate 1000 neurons on one core, hence 1% atm). Each has about 7000 connections, or alternatively there are estimated 10^15 connections or more.
The internet has only about 15-20 billion nodes, only few highly connected, the Internet is more of a tree than a complete mesh.
So in some way, you could say we are nowhere close to obtaining the computing power of the brain, if we, of course, disregard the fact that the nodes, computers, are very powerful on their own. But this is where the comparison breaks. The human brain is powerful due to the complexity of its network, not the sheer amount of "working units" (cores/transistors).
Comment on the project: it is exciting, but it must not be understated that it is still a very gross approximation, since the neuron models used are simple. It might be the case that interesting behavior will arise using this simple model, but it also might be the case that the secret sauce is in the fine behavior of each neuron. The hope is that interesting phenomena can be observed with this simpler model, perhaps a bit like classical physics can often be used without requiring the full model of quantum mechanics.
It's also very important to remember how incredibly slow the human brain is. Were talking 100s of ms slow to go from sensory input to motor output. A single neuron might take between 1 and 10 ms to fire, and a single dendrite might take 1/10th that time, so at best your doing computation at 10khz. CPU has 5 orders of magnitude over biology.
The problem is we have only a vague idea of how the network is connected and don't really know the algorithm thats being implemented by that network. So we fall back on things like simulating ion channels which take way more compute resources than necessary. There is a lot of cargo culting going on right now, but of course it's also insanely exciting and fun to find out what does and doesn't work.
This, he says, requires about 10 racks. So a human brain would require 1000 racks, or about half a typical Amazon data center, of which Amazon has more than a hundred.
We'll have to see if he can do anything useful with all those CPUs.
That statement also requires an assumption that there's a logical one-to-one mapping between an organic nueron and an ANN neuron. In reality, ANN's are modeled using very loose approximations of organic neurons. For all we know, within each organic neuron, there are billions of parallel 'threads' of 'computation' or there are links between neurons that a traditional model doesn't capture. Or any number of things. We're simply too far from understanding what actually happens in brains to even start talking about computational equivalency.
not exactly. The most of the "thinking" neurons form the surface of the brain - that surface is wrinkled into "gyri" to achieve more than 2D, yet it isn't 3D. In the language of Hausdorff dimension it is 2.x something.
And the neuron connections are pretty structured topology that is far from each-to-each. If, for example, you look at this picture https://en.wikipedia.org/wiki/Fornix_%28neuroanatomy%29#/med... you can see how the wires from the neurons of the hippocampus gyrus are bundled together into the "fimbria" and routed through the "fornix".
From:
edit: thanks for the replies, did some research found this TED talk that visualizes the problem pretty well https://www.youtube.com/watch?v=HA7GwKXfJB0
- inferences of neuron interactions from electronic wiring individual neurons in mice brains;
- fMRI and tFMI (a mathematical extension that associates area that are activated one after the other) on live humans;
- scanning ultra-thin slices (volunteer human brain donor) and using that for an neuron-to-synapse complete map of at least one brain. Both the slicing technology and the AI to re-combine images into not just a 3D model, but a complete connectron are pretty mind-blowing achievements.
If you combine that to the possible impact in AI and medical learnings on how things like degenerescence, you can imagine why it ends up feeling far more ambitious than the Manhattan project.
>The control interface is two 100Mbps Ethernet connections, one for the Board Management Processor and the second for the SpiNNaker array. There are options to use the six on-board 3.1Gbps high-speed serial interfaces (using SATA cables, but not necessarily the SATA protocol) for I/O...