人工進化研究所（AERI）

Mar 187 min read

Brain-Implanted Biocomputer Intelligent Processor Chipsets- Cranial Nerves Connected Synaptic Channel type MOS transistor LSI -

Professor Kamuro's near-future

science predictions

Brain-Implanted Biocomputer Intelligent Processor Chipsets

- Cranial Nerves Connected Synaptic Channel type MOS transistor LSI -

Physicist and Brain Scientist

Visiting Professor of Quantum Physics,

California Institute of Technology

IEEE-USA Fellow

American Physical Society-USA Fellow

PhD. & Dr. Kazuto Kamuro

AERI：Artificial Evolution Research Institute

Pasadena, California

HP: https://www.aeri-japan.com/

and

Xyronix Corporation

Pasadena, California

HP: https://www.usaxyronix.com/

Foreword

A. Professor Kamuro's near-future science predictions, provided by CALTECH professor Kazuto Kamuro(Doctor of Engineering (D.Eng.) and Ph.D. in Quantum Physics, Semiconductor Physics, and Quantum Optics), Chief Researcher at the Artificial Evolution Research Institute (AERI, https://www.aeri-japan.com/) and Xyronix Corporation(specializing in the design of a. Neural Connection LSI, b. BCI LSI（Brain-Computer Interface LSI） (Large Scale Integrated Circuits) , and c. bio-computer semiconductor technology that directly connects bio-semiconductors, serving as neural connectors, to the brain's nerves at the nano scale, https://www.usaxyronix.com/), are based on research and development achievements in cutting-edge fields such as quantum physics, biophysics, neuroscience, artificial brain studies, intelligent biocomputing, next-generation technologies, quantum semiconductors, satellite optoelectronics, quantum optics, quantum computing science, brain computing science, nano-sized semiconductors, ultra-large-scale integration engineering, non-destructive testing, lifespan prediction engineering, ultra-short pulses, and high-power laser science.

The Artificial Evolution Research Institute (AERI) and Xyronix Corporation employ over 160 individuals with Ph.D.s in quantum brain science, quantum neurology, quantum cognitive science, molecular biology, electronic and electrical engineering, applied physics, information technology (IT), data science, communication engineering, semiconductor and materials engineering. They also have more than 190 individuals with doctoral degrees in engineering and over 230 engineers, including those specializing in software, network, and system engineering, as well as programmers, dedicated to advancing research and development.

Building on the outcomes in unexplored and extreme territories within these advanced research domains, AERI and Xyronix Corporation aim to provide opportunities for postgraduate researchers in engineering disciplines. Through achievements in areas such as the 6th generation computer, nuclear deterrence, military unmanned systems, missile defense, renewable and clean energy, climate change mitigation, environmental conservation, Green Transformation (GX), and national resilience, the primary objective is to furnish scholars with genuine opportunities for learning and discovery. The overarching goal is to transform them from 'reeds that have just begun to take a step as reeds capable of thinking' into 'reeds that think, act, and relentlessly pursue growth.' This initiative aims to impart a guiding philosophy for complete metamorphosis and to provide guidance for venturing into unexplored and extreme territories, aspiring to fulfill the role of pioneers in this new era.

B. In the cutting-edge research domain, the Artificial Evolution Research Institute (AERI) and Xyronix Corporation have made notable advancements in various fields. Some examples include:

1. AERI･HEL (Petawatt-class Ultra-High Power Terawatt-class Ultra-High Power

Femtosecond Laser)

◦ Petawatt-class ultra-high power terawatt-class ultra-short pulse laser (AERI･HEL)

2. 6th Generation Computer&Computing

◦ Consciousness-driven Bio-Computer

◦ Brain Implant Bio-Computer

3. Carbon-neutral AERI synthetic fuel chemical process

(Green Transformation (GX) technology)

◦ Production of synthetic fuel (LNG methanol) through CO₂ recovery system (DAC)

4. Green Synthetic Fuel Production Technology(Green Transformation (GX) technology)

◦ Carbon-neutral, carbon-recycling system-type AERI synthetic fuel chemical process

5. Direct Air Capture Technology (DAC)

◦ Carbon-neutral, carbon-recycling carbon dioxide circulation recovery system

6. Bio-LSI・Semiconductors

◦ Neural connection element directly connecting bio-semiconductors and brain nerves

on a nanoscale

◦ Brain LSI Chip Set, Bio-Computer LSI, BMI LSI, BCI LSI, Brain Computing LSI,

Brain Implant LSI

7. CHEGPG System (Closed Cycle Heat Exchange Power Generation System with

Thermal Regenerative Binary Engine)

◦ Power generation capability of Terawatt (TW), annual power generation of

　　　　10,000 TWh (terawatt-hour) class

◦ 1 to 0.01 yen/kWh, infinitely clean energy source, renewable energy source

8. Consciousness-Driven Generative Autonomous Robot

9. Brain Implemented Robot･Cybernetic Soldier

10. Generative Robot, Generative Android Army, Generative Android

11. High-Altitude Missile Initial Intercept System, Enemy Base Neutralization System,

　 Nuclear and Conventional Weapon Neutralization System, Next-Generation

　　Interception Laser System for ICBMs, Next-Generation Interception Laser System

　　　 for Combat Aircraft

12. Boost Phase, Mid-Course Phase, Terminal Phase Ballistic Missile Interception System

13. Volcanic Microseismic Laser Remote Sensing

14. Volcanic Eruption Prediction Technology, Eruption Precursor Detection System

15. Mega Earthquake Precursor and Prediction System

16. Laser Degradation Diagnosis, Non-Destructive Inspection System

　 17. Ultra-Low-Altitude Satellite, Ultra-High-Speed Moving Object

　　　 Non-Destructive Inspection System

✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼

Brain-Implanted Biocomputer Intelligent Processor Chipsets

- Cranial Nerves Connected Synaptic Channel type MOS transistor LSI -

Recent strides in the realm of artificial intelligence (AI), propelled by programmatic innovations, have ignited a fervor among researchers aiming to engineer computers mirroring the intricate functionality of the human brain. Contemporary digital computing systems, characterized by a non-Von Neumann architecture housing distinct processing and storage units, find themselves grappling with the intensifying energy demands of high-density data tasks. Against the backdrop of smart devices perpetually amassing data, researchers diligently explore novel approaches to data processing while addressing the ever-escalating power consumption.

Persisting challenges confront existing digital computing systems, primarily the persistent dilemma of heightened energy consumption entwined with the act of switching. For decades, the semiconductor integration engineering paradigm has leaned heavily on transistors and a consistent silicon architecture, witnessing substantial progress with the integration of digital transistors into LSI. However, this approach bears the substantial cost of amplified energy consumption, particularly in today's era of big data, where digital computing for AI and machine learning tasks holds the potential to strain power infrastructures. Hence, a compelling mandate emerges for the reassessment of non-Von Neumann-type computing hardware.

Within digital computers, data shuttles back and forth between microprocessors and memory, incurring substantial energy expenditure. Attempts to execute multiple tasks simultaneously only compound the bottleneck issue. In stark contrast, the human brain seamlessly intertwines memory and information processing in the same locale, resulting in remarkable energy efficiency. Drawing inspiration from this biological marvel, researchers have birthed the Cranial Nerves Connected Synaptic Channel type MOS transistor LSI, ushering in an era of higher-order thinking.

the Cranial Nerves Connected Synaptic Channel type MOS transistor LSI effortlessly undertakes memory and information processing functions simultaneously, authentically mirroring the cognitive processes of the brain. This LSI establishes connections with neural networks through the AERI BMI (Brain-Implanted Machine Interface) VLSI chipset, giving rise to the AERI brain-implanted biocomputer intelligent processor chipsets. Beyond executing simple machine learning tasks, these chipsets demonstrate the prowess of associative learning. Operating steadfastly at room temperature, the Cranial Nerves Connected Synaptic Channel type MOS transistor LSI minimizes energy consumption and retains information even when disconnected from power.

In the advancement of AERI’s deep learning generative AI systems, the Cranial Nerves Connected Synaptic Channel type MOS transistor LSI takes a profound leap forward. AERI’s deep learning generative AI systems master the fundamental task of classifying data—an essential feat for emulating human thought. The ultimate ambition is to propel AERI’s deep learning generative AI systems technology toward higher-order thinking, encompassing advanced cognition. Augmented with BMI (Brain Machine Interface), the Cranial Nerves Connected Synaptic Channel type MOS transistor LSI incorporates a groundbreaking brain-like transistor comprising 10 billion elements. This constitutes a consciousness-driven bio-computer architecture (non-Von Neumann-type) intricately connected to neural networks, realizing Generative Artificial Intelligence based on deep learning at room temperature.

In parallel, researchers, drawing inspiration from the intricacies of the human brain, introduce a cutting-edge Cranial Nerves Connected Synaptic Channel type MOS transistor LSI. the AERI Cranial Nerves Connected Synaptic Channel type MOS transistor LSI not only concurrently processes and stores information, emulating the capabilities of the human brain, but also showcases an impressive capacity for associative learning.

In a departure from previous endeavors to create consciousness-driven biocomputing devices resembling the brain, the AERI cranial nerves connected synaptic channel type MOS transistor operates seamlessly at room temperature, circumventing the need for cryogenic conditions. Its remarkable speed, minimal energy consumption, and the ability to retain information even without power position it as a promising candidate for diverse practical applications.

Professor Kamuro, instrumental in this groundbreaking development, accentuates the fundamental distinctions between the architecture of the brain and traditional digital computers. He underscores the energy efficiency achieved by the AERI cranial nerves connected synaptic channel type MOS transistor, accentuating its integration of memory and artificial information processing—a harmony reminiscent of the human brain.

As artificial intelligence continues its march forward, there arises a surging demand for computers that faithfully emulate human brain functionality. Traditional digital consciousness-driven biocomputing systems grapple with challenges stemming from the separation of processing and storage units, resulting in energy-intensive operations. the AERI cranial nerves connected synaptic channel type MOS transistor aspires to surmount these challenges, representing a quantum leap in the quest for energy-efficient and high-performance computing tailored for AI and machine-learning tasks.

the AERI cranial nerves connected synaptic channel type MOS transistor undergoes rigorous testing, unveiling its adeptness in recognizing and categorizing similar patterns through associative learning. Professor Kamuro accentuates the device's potential for higher-level thinking in AI technology, addressing the intricacies of real-world conditions that frequently stymie existing algorithms.

In experimental scenarios, the AERI cranial nerves connected synaptic channel type MOS transistor exhibits robust performance, even when presented with incomplete patterns—an exhibition of its resilience in associative learning. Professor Kamuro underscores the device's potential applications in scenarios where current AI systems might falter, such as adverse weather conditions impacting self-driving vehicles.

The revelation of the AERI cranial nerves connected synaptic channel type MOS transistor marks a monumental breakthrough in the realm of consciousness-driven biocomputing hardware, charting the course for more energy-efficient and advanced AI technologies.

END

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Quantum Brain Chipset & Bio Processor (BioVLSI)