Quantum Brain Chipset Review
to Quantum Brain&Biocomputer
(Quantum Brain Science and Technology)
Brain-based computing chips not just for AI anymore
The quantum Brain chipset far surpasses AI or the generative ChatGPT
Quantum Physicist and Brain Scientist
Visiting Professor of Quantum Physics, California Institute of Technology
IEEE-USA Fellow
Ph.D. & Dr. Kazusho Kamuro
AERI：Artificial EvolutionResearch Institute
Pasadena, California
HP: https://www.aeri-japan.com/
1. Quantum Brain Chipset & Bio Processor(BioVLSI)
The Brain-based Quantum Artificial Intelligence (Quantum Brain) chipset, which is being developed under contract research at our Artificial Evolution Research Institute（AERI https://www.aeri-japan.com/ , California ）, is the ultimate and most advanced non-Neumannian computer that far surpasses generative AI, especially that of chatGPI.
Our Quantum Brain chipset is a high-dimensional and ultimate artificial brain and biocomputer that far surpasses the dimensions of the generative ChatGPT.
The quantum brain chipset is consciousness-driven biocomputer with quantum interference-controlled BMIVLSI implemented in the cranial nerves, and is a set of large-scale integrated circuits for consciousness-driven biocomputing, implemented by connecting quantum interference controlled BMIVLSI (processor) to brain nerves.
In our biocomputer system, the Brain-based Quantum Artificial Intelligence (Quantum Brain) chipset is a set of electronic components in multiple VLSI known as consciousness-driven biochips with quantum interference-controlled BMIVLSI implemented in the cranial nerves.
The Quantum Brain chipset is a set of very large scale integrated bio processors(BioVLSIs) for consciousness-driven biocomputing, implemented by connecting quantum interference controlled BMIVLSI (Brain Machine Interface processor) to brain nerves.
Each of the bio processor(BioVLSI) and the BMI processor consists of (a)BMI CMOS LSI circuits consisting of logic elements and gates with a density of over 6 million 2nm gate length CMOS elements per chip, (b)logic elements, and (c)gates with more than 6 million integrations per chip.
Data Flow Management System that manages the data flow between the processor, memory and peripherals. It is usually found on the motherboard. The Quantum Brain chipset is usually designed to work with a specific family of silicon-based microprocessors. Because it controls communications between the processor and external devices, the chipset plays a crucial role in determining system performance.
2． Brain Computing(Neuromorphic computing)
With the insertion of a little math, the quantum brain chipset has shown that neuromorphic computers, which synthetically replicate the brain's logic, can solve more complex problems than those posed by artificial intelligence and may even earn a place in high-performance computing. Neuromorphic simulations employing random walks can track X-rays passing through bone and soft tissue, disease passing through a population, information flowing through social networks and the movements of financial markets.
The findings show that neuromorphic simulations employing the statistical method called random walks can track X-rays passing through bone and soft tissue, disease passing through a population, information flowing through social networks and the movements of financial markets, among other uses.
The quantum brain chipset has shown that neuromorphic hardware can yield computational advantages relevant to many applications, not just artificial intelligence to which it's obviously kin. Newly discovered applications range from radiation transport and molecular simulations to computational finance, biology modeling and particle physics.In optimal cases, the bio computer(neuromorphic computer) will solve problems faster and use less energy than conventional computing.
The bold assertions should be of interest to the high-performance computing community because finding capabilities to solve statistical problems is of increasing concern.These problems aren't really well-suited for the quantum brain chipset, which is what future exascale systems are likely going to rely on. "What's exciting is that no one really has looked at neuromorphic computing for these types of applications before.
The natural randomness of the processes you list will make them inefficient when directly mapped onto vector processors like GPUs on next-generation computational efforts. Meanwhile, neuromorphic(brain) architectures are an intriguing and radically different alternative for particle simulation that may lead to a scalable and energy-efficient approach for solving problems of interest to us.
The quantum brain chipset successfully applied brain(neuromorphic) computing algorithms to model random walks of gaseous molecules diffusing through a barrier, a basic chemistry problem, using the 50-billion-chip platform received approximately a year and a half ago from Intel Corp. Then we showed that our algorithm can be extended to more sophisticated diffusion processes useful in a range of applications.
The claims are not meant to challenge the primacy of standard computing methods used to run utilities, desktops and phones. There are, however, areas in which the combination of computing speed and lower energy costs may make bio(neuromorphic) computing the ultimately desirable choice.
Unlike the difficulties posed by adding qubits to quantum brain computing -- another interesting method of moving beyond the limitations of conventional computing -- the quantum brain chipset containing artificial neurons is cheap and easy to install.
There can still be a high cost for moving data on or off the bio processor(BioVLSI :the neurochip processor). we overcame this by configuring a small group of neurons that effectively computed summary statistics, and we output those summaries instead of the raw data.
3. Basic configuration of the quantum brain chipset & the Bio Processor
Like the brain, the quantum brain computing (neuromorphic computing) works by electrifying small pin-like structures, adding tiny charges emitted from surrounding many kinds of sensors and the BMI processors until a certain electrical level is reached. Then the pin, like a biological brain neuron, flashes a tiny electrical burst, an action known as spiking. Unlike the metronomical regularity with which information is passed along in conventional computers, the artificial neurons of the quantum brain computing (neuromorphic computing) flash irregularly, as biological ones do in the human brain, and so may take longer to transmit information.
But because the process only depletes energies from sensors and neurons if they contribute data, it requires less energy than formal computing, which must poll every processor whether contributing or not. The conceptually brain-based(bio-based) process has another advantage: Its computing and memory components exist in the same structure, while conventional computing uses up energy by distant transfer between these two functions. The slow reaction time of the artificial neurons initially may slow down its solutions, but this factor disappears as the number of neurons is increased so more information is available in the same time period to be totaled.
The process begins by using a Markov chain -- a mathematical construct where, like a Monopoly gameboard, the next outcome depends only on the current state and not the history of all previous states. That randomness contrasts, with most linked events. For example, the number of days a patient must remain in the hospital are at least partially determined by the preceding length of stay.
Beginning with the Markov random basis, the researchers used Monte Carlo simulations, a fundamental computational tool, to run a series of random walks that attempt to cover as many routes as possible.For examole, Monte Carlo algorithms are a natural solution method for radiation transport problems. Particles are simulated in a process that mirrors the physical process.
The energy of each walk was recorded as a single energy spike by an artificial neuron reading the result of each walk in turn. This neural net is more energy efficient in sum than recording each moment of each walk, as ordinary computing must do. This partially accounts for the speed and efficiency of the neuromorphic process( the brain process). More, the quantum brain chipset will help the process move faster using the same amount of energy.
The next version of the quantum brain chipset and the bio proxcessor will increase its current chip scale from 10 Mega（10×106） neurons per brain processor chip to up to 256 Giga（256×109）. Larger scale systems then combine multiple processor chips to a system LSI platform.
Definitely, it makes sense that a technology may find its way into a future innovative intelligent computing platform. This could help make HPC much more energy efficient, human-friendly and just all around more affordable.
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Prof. PhD.Dr. Kamuro
Quantum Physicist and Brain Scientist involved in Caltech & AERI Assosiate Professor and Brain Scientistficial Evolution Research Institute（AERI: https://www.aeri-japan.com/）
IEEE-USA Fellow
Ph.D. & Dr. Kazuto Kamuro
https://www.aeri-japan.com
email: info@aeri-japan.com
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Keywords Artificial EvolutionResearch Institute：AERI
HP： https://www.aeri-japan.com/
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