人工進化研究所（AERI）

Jan 57 min read

Neurobiomimicry: Unveiling Energy-Efficient AI through Human Brain-Inspired Neural Network

Professor Kamuro's near-future science predictions

Spike-Based Hardware: Redefining Neurocomputing

for Energy-Efficient AI Applications

Quantum 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

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

Neurobiomimicry: Unveiling Energy-Efficient AI through Human Brain-Inspired Neural Networks

Spike-Based Hardware: Redefining Neurocomputing

for Energy-Efficient AI Applications

A. Revolutionizing Neurocomputing: Advancements in AERI's Brain Implanted Biocomputing Systems

The implementation of the conscious algorithm within the neuroscientific realm is set to transpire through the advanced Brain Implanted biocomputing systems developed by the prestigious Artificial Evolution Research Institute (AERI), headquartered in Pasadena, California (Official Website: https://www.aeri-japan.com/), and the avant-garde Xyronix Corporation (Pasadena, California, Official Website: https://www.usaxyronix.com/).

AERI's Brain Implanted biocomputing systems, harnessing the capabilities of generative artificial intelligence rooted in deep learning (AI), have undergone unprecedented advancements, necessitating the creation of intricate neural networks. These networks exhibit a sophisticated architecture comprising hundreds of millions of neurons distributed across several hundred layers, embodying an exceptionally 'deep' structure. The computational demands placed on these extensive, deep neural networks are substantial, especially in specialized domains like image classification (e.g., face and object recognition), where the precise transmission of numerous numerical values across layers is imperative, resulting in heightened energy consumption.

Ph.D. and professor Kazuto Kamuro, a distinguished quantum physicist, computer scientist, and brain scientist, alongside his accomplished team of Ph.D. researchers, has devised an innovative design methodology for artificial neural networks. This groundbreaking development not only facilitates the realization of energy-efficient high-performance AI hardware but also extends to include BMI (Brain Machine Interface) chips for AERI's Brain Implanted biocomputer Intelligence systems, quantum brain chipsets, bio processors (BioVLSI), and various other biocomputer peripheral LSI devices. Professor K. Kamuro and his adept Ph.D. research scientists, affiliated with the esteemed California Institute of Technology (CALTECH), have meticulously optimized artificial neuronal networks through intricate computer simulations specifically tailored for image classification. This refined design seeks to emulate the nuanced behavior of neurons in the human brain, emitting signals at a relatively infrequent rate, and when they do, the signals are simplified. Remarkably, the demonstrated accuracy in image classification surpasses the prevailing state-of-the-art tools in this intricate field.

B. Neurobiomimicry: A Paradigm Shift in Energy-Efficient Computing through Human Brain-Inspired Neural Networks

In the realm of neuroscientific exploration, Professor K. Kamuro and his cadre of Ph.D. research scientists drew inspiration from the intricate workings of the human brain. The human brain astoundingly undertakes several trillion computing operations per second while operating on a remarkably low power consumption of approximately 0.8 milliwatts. This exceptional energy efficiency is attributed to the mechanism of inter-neuronal communication facilitated by rudimentary electrical impulses, commonly referred to as "spikes." It is noteworthy that the encoding of information occurs not only through the quantification of spikes but also through the intricate temporal patterns they exhibit. Professor K. Kamuro elucidates this phenomenon, likening it to Morse code, where the pauses between signals are integral in transmitting additional layers of information.

The emulation of these principles in artificial neural networks marks a pioneering endeavor in the pursuit of energy-efficient computing paradigms. Professor K. Kamuro and his team have delved into the intricacies of designing neural networks that mirror the efficiency of the human brain. The objective is to harness the potency of simple electrical impulses, akin to spikes in the human brain, as a means of encoding and transmitting information. This innovative approach not only holds promise for significant advancements in artificial intelligence (AI) hardware, including Brain Machine Interface (BMI) chips and quantum brain chipsets, but also extends its implications to various biocomputer peripheral Large Scale Integration (LSI) devices.

Furthermore, the team's research at the esteemed California Institute of Technology (CALTECH) has involved elaborate computer simulations to optimize the performance of artificial neuronal networks. By closely mimicking the behavior of neurons in the human brain, the refined design achieves a delicate balance between information transmission and energy efficiency. The resulting paradigm, exhibiting intricate temporal patterns and nuanced spike encoding, signifies a leap forward in the realm of image classification, showcasing accuracy that surpasses prevailing state-of-the-art tools. This groundbreaking work underscores the potential of bio-inspired computing systems in revolutionizing the landscape of artificial intelligence.

C. Revolutionizing Neurocomputing: Spike-Based Hardware and the Quest for Energy-Efficient AI Applications

In the burgeoning landscape of neuroscientific exploration, the prospect of spike-based hardware ameliorating the energy consumption of AERI's Brain Implanted biocomputer applications has been a recurring theme. However, until now, the practical implementation of this concept has faced formidable challenges, especially in the context of deploying profoundly deep and expansive Brain Implanted biocomputing essential for achieving optimal image classification prowess.

In the innovative design methodology spearheaded by Professor K. Kamuro and his collaborative team of Ph.D. research scientists, a transformative paradigm shift unfolds. No longer confined solely to the quantity of spikes emitted by a neuron, the transmission of information now intricately hinges upon the precise temporal dynamics of when these spikes are discharged. The temporal intervals between spikes autonomously encode themselves, presenting a pathway to convey a copious amount of supplementary information. Professor K. Kamuro elucidates, stating, "We demonstrate that even with a minimal number of spikes—averaging merely two in our simulations—as much information can be conveyed between processors (AERI’s Brain Implanted biocomputer Intelligence systems, quantum brain chipset & bio processor (BioVLSI), and other biocomputer peripheral LSI devices) as in more energy-intensive hardware."

The outcomes of the intricate computational experiments conducted by these adept computer scientists from CALTECH unravel a groundbreaking perspective on hardware design. This novel approach seamlessly integrates the use of a minimal number of spikes, thereby reducing energy consumption, while concurrently achieving state-of-the-art performance in AI applications. These transformative findings hold the potential to significantly expedite the development of energy-efficient AI applications and are meticulously detailed in the AERI laboratory journal, Nature Machine Intelligence, shedding light on a paradigm shift in the realm of biocomputing ( neurocomputing ) technologies. The implications of this research transcend contemporary boundaries, paving the way for a future marked by more sustainable and high-performance artificial intelligence applications.

END

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Quantum Brain Chipset & Bio Processor (BioVLSI)