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H+ magazine wrote an estimate in 2009 that seems broadly comparable to other things I've seen; they think the human brain is approximately 37 petaflops. A supercomputer larger than that 37 petaflop estimate exists today. But emulation is hard. See this SO question about hardware emulation or this article on emulating the SNES, in which they require 140 ...


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Quick Answer When Intel acquired Nirvana, they indicated their belief that analog VLSI has its place in the neuromorphic chips of the near future1, 2, 3. Whether it was because of the ability to more easily exploit the natural quantum noise in analog circuits is not yet public. It is more likely because of the number and complexity of parallel activation ...


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I think, there are various reasons. First of all: Flexibility. With modern CPUs and GPUs you can construct pretty much every AI model you want and in every size and complexity you want. How can you be sure that the model you are currently using is still suitable in a few years? Maybe there will be a major breakthrough in NNs in the next few years? Maybe some ...


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Human brain contains about 100 billions neurons (10^11) and about hundred trillions synapses ($10^14). Each neuron can fire about 100 times a second. If we model brain as a simple neural network, then it would be equivalent to machine that requires 1016 calculations per second and 1013 bits of memory. From Wikipedia Kurzweil introduces the idea of "...


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Vernor Vinge said that if we can scan a human brain and then simulate it: We can run it at 1000 times the speed. The brain will be able to do 1000 years of thinking in 1 year ect. At this stage in history we have the computer power. The trouble lies in cutting a brain up and scanning the 100 billion neurons and 12 million kilometres of axons and 100000 ...


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Second question first: Data is stored in an ANN in the form of weights in the adjacency matrix between neurons. During training, these weights are updated by a learning algorithm (such as backpropagation). First question: according to award-winning neuroscientist Tim Bliss: “It’s been accepted really since the turn of the 20th century, since the time of ...


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State of Rosehip Research The Rosehip neuron is an important discovery, with vast implications to AI and its relationship to the dominant intelligence on earth for at least the last 50,000 years. The paper that has spawned other articles is Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type, Buldog et. al.,...


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Tensor operations The major work in most ML applications is simply a set of (very large) tensor operations e.g. matrix multiplication. You can do that easily in an ASIC, and all the other algorithms can just run on top of that.


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It looks like you really have two questions here. I'll try to answer the first one, and you should think about making a separate question for the second. There is research into using simulated models of biologically realistic neurons. While there are large projects like the Human Brain Project aimed at simulating human brains, there is also a lot of lower-...


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I am surprised no one mentioned some of the specific research directions in the analog AI field. And also to clarify Artificial Intelligence is not exactly the same as Machine Learning as this answer suggests. Recent advances in analog computation has only been in the field of Machine Learning. Analog CMOS: First off let us talk about the earliest analog ...


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Digital Instrumentation of the Analog Cells One of the key challenges in analog artificial networks is that network instrumentation would be most practical if digital. Any VLSI implementation of analog perceptrons, convolutions, or spiking networks will likely need to have digital components in a hybrid arrangement for several functions. Health indicators ...


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"Principles of Computational Modelling in Neuroscience" by David Sterratt, Bruce Graham, Andrew Gillies and David Willshaw discuss it in Chapter 7 (The synapse) and also in Chapter 8 (Simplified models of neurons). Especially in chapter 8, they discuss how to add excitatory or inhibitory sysnapses into integrate and fire neuron. There are various ways to ...


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I think the algorithm has changed minimally, but the necessary hardware has been trimmed to the bone. The number of gate transitions are reduced (perhaps float ops and precision too), as are the number of data move operations, thus saving both power and runtime. Google suggests their TPU achieves a 10X cost saving to get the same work done. https://...


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Neuromorphic engineering offers various of ways of reproducing the brain’s processing ability. The recent technology can include IBM's multi-artificial-neuron computer, the world's first artificial nanoscale stochastic phase-change neuronsarticle. Check the: Stochastic phase-change neurons study. Other can include Neurogrid, built by Brains in Silicon at ...


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It is true that the current Machine learning is based on treating neurons as a component in the whole complexity , mesh of neurons. The focus is more on the architecture rather than understanding or imitating the basic block of it more clearly , i.e. the neurons. Anirban Bandhopadhyay is a biologist and Neurologist who has studied how the harmony changes ...


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I believe that most people have pretty much answered the question diligently in a really informative way. I would just like to say that we use digital circuits commonly because that is the existing technology and that definitely analog circuits seem really promising. However, at this moment, this idea is not very well-developed despite the amount of ...


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No. Reasons include, but are not limited to: lack of understanding of how the brain works current ANNs are mostly good at pattern recognition and generative tasks, but lack capacity to create abstractions on their own we cant match size/number of perceptrons to number of neurons even with much smaller ANN size network, performance is an issue (i.e. state ...


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Not just how much, but what kind of processing power : there're specially-crafted dedicated chips, and it has a practical applications, so it's not a lab-only project


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In biology, when the presynaptic releases a neurotransmitter (a positive amount of them, obviously), this neurotransmitter reaches the postsynaptic vesicles causing an excitatory (depolarization) or inhibitory (hyperpolarization) effect, depending on the kind of postsynaptic vesicle in next cell dendrites. If the total amount of depolarization (all dendrites)...


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One can also approach the question from the information theory aspect: There are two trade/offs to choose from: Analog information that may represent information in a more precise/specific way, but limited in quantity. Digital information that doesn't fully represent the real world, but may contain unlimited amount of information within a few bits. A good ...


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Hava Siegelmann On the first look Analog computing is superior to digital one. Quantum computers are faster than Von-Neumann computers and neuromorphic chips need less energy than Intel CPUs. Also from a theoretic point of view many speaks for analog computers. Hava Siegelmann has researched the Super-turing capability of neural network, which means that an ...


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The Degree to Which Inhibition is in Common Use What could loosely be considered inhibitory effect occurs in MLPs (multilayer perceptrons) as they are normally designed and implemented already. The gradient descent scheme implemented within a larger back propagation algorithm can produce a parameter adjustment delta that is either positive or negative. A ...


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It's been done (essentially). This guy at the following link has used a series of FPGAs to emulate hundreds of 8080s, using them to train a neural network to play Gameboy games. https://towardsdatascience.com/a-gameboy-supercomputer-33a6955a79a4 IBM's True North being used in Darpa's SyNAPSE program is also very close to what you suggest. https://en....


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ASIC - It stands for Application Specific IC. Basically you write programs to design a chip in HDL. I'll take cases of how modern computers work to explain my point: CPU's - CPU's are basically a microprocessor with many helper IC's performing specific tasks. In a microprocessor there is only a single Arithmetic Processing unit (made up term) called ...


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