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生体ゆらぎに学ぶ超低消費電力を実現する次世代AIデバイス Next-generation AI Devices Learned from Biological Fluctuations for Realizing Ultra-low Electric Power Consumption



研究リーダー Project Leader
田畑 仁 教授 Hitoshi Tabata Professor
東京大学 大学院工学系研究科 Graduate School of Engineering, The University of Tokyo
研究者 Researchers
  • 飯塚 哲也 准教授 Tetsuya Iizuka Associate Professor
  • 関 宗俊 准教授 Munetoshi Seki Associate Professor
  • 福島 鉄也 特任准教授 Tetsuya Fukushima Project Associate Professor
  • 山原 弘靖 助教 Hiroyasu Yamahara Assistant Professor
研究生 Research Student
長田 将 Masaru Osada 松岡 英 Akira Matsuoka Sarker Md Shamim Sarker Md Shamim Liao Zhiqiang Liao Zhiqiang Zhang Wenjiong Zhang Wenjiong Park Han Sol Park Han Sol 杉本 雛乃 Hinano Sugimoto







ムーアの法則の限界 Limitations of Moore’s Law

“ムーアの法則”に限界が見えてきました。 ムーアの法則とは、1965年にインテルの共同創始者であるゴードン・ムーア氏が「一つのチップ上の半導体の集積率は18カ月ごとに倍増する」として半導体技術の進化を予測した指標です。過去50年の間、半導体はこの予測の通り微細化・集積化を続けコンピューティングの発展を規定してきました。近年のAIの台頭もこの計算機の性能向上の恩恵に寄るところが大きいと言えます。しかし近年、いよいよ半導体の微細化・集積化には物理的、エネルギー効率的な限界が近づいています。現代の先端半導体プロセッサは5nmプロセス技術で製造されています。この先も集積化技術は進展していくと思われますが、これまでのような指数関数的な性能向上は期待できなくなっています。また、消費電力の問題はより大きな課題として顕在化しています。半導体の技術革新による省電力化は約10年前から下げ止まりになっており、経済産業省によると2050年には総消費電力の約60%をICT機器が占めるに至ると予測されています。このような状況の中、更なるAIの進化の為には、従来とは全く異なる新しい発想でのコンピューターの開発、特に動作時の超低消費電力化や待機電力低減を実現する「省エネルギー」技術革新が喫緊の課題となっています。

The limits of Moore’s Law are insight. Moore’s Law is an index used by Gordon Moore, the co-founder of Intel Corporation, to predict the evolution of semiconductor technology, saying, “The number of transistors on a microchip doubles every 18 months.” Over the last 50 years, semiconductors have continued to miniaturize and become integrated as predicted, shaping computing progress. The recent rise of artificial intelligence (AI) may also depend considerably on the benefits of improving computer performance. In recent years, however, the miniaturization and integration of semiconductors have shown signs of approaching their physical and energy efficiency limits. Modern advanced semiconductor processors are manufactured with 5 nm process technology. Although integrated technology will continue advancing, exponential performance improvement is becoming hard to expect. Power consumption emerges as a serious issue. In fact, energy saving through the technological innovation of semiconductors has remained stagnant for about a decade. The Ministry of Economy, Trade and Industry projects that information-communication technology equipment will account for about 60% of the total power consumption by 2050. For AI to further evolve in this situation, there is a need to develop computers based on an entirely new concept. Particularly, the innovation of energy saving technologies achieving ultra-low power consumption and standby power reduction during operations is an urgent task.


Details of Project

従来型コンピューターの壁を越える研究 Research Crossing Barriers of Conventional Computers


This project aims to develop next-generation computers replacing conventional machines, that employ a new mechanism not using electron charges for transmitting and processing information. We will particularly focus on the “fluctuations” of living organisms and develop next-generation ultra-low power consumption computers based on the design guidelines for actively making use of noise, considered a “nuisance” in the past.

[1]スピントロニクス(マグノニクス)による超低消費電力コンピューター [1] Ultra-low power consumption computers based on spintronics


Existing electronic devices have used electric charge, a property of electrons and a source of electricity, to transmit and control information. Controlling the flow of electric charge (current), the “electronics” technology has supported the evolution of electronic devices. However, as Moore’s Law approaches its limits, attention is turning to another property of electrons, the intrinsic spin of the electron serving as the source of magnetism. Spin holds the potential to solve computer energy problems. Accordingly, research and development are actively promoted as the study of intrinsic spin, or “spintronics/magnonics,” worldwide.
In electronics (the study of electric current), the transmission and control of information involve the actual transportation of electrons. Heat generation is thus unavoidable combined with large energy loss. On the other hand, spin angular momentum transmission and control apply the phenomenon called spin wave. The angular momentum of electrons (a quantity representing the momentum of rotational motion) is transmitted as a wave, allowing information to be transmitted and controlled without transporting electrons. Consequently, zero heat is generated. Based on this property, spin is expected to be applied to next-generation ultra-low power consumption computers that can transmit and control information without heat loss. Our research team has already produced the world’s first high-temperature glass material for new spin wave elements. By maintaining and accelerating the advantage, we aim to solve the problem of power consumption of conventional computers, and develop highly reliable computers with innovative low power consumption.
Specifically, our research and development focus on spin wave elements using the magnetic garnet, known as a jewel, to develop brain-type computers (neuromorphic computing) mimicking the behavior of the human brain. We hope to apply the results of this project to quantum computing and reservoir computing by spin phase interference.

[2]ノイズを利用するという逆転の発想 [2] Reversed concept applying noise

本研究の特徴的なアプローチとして “確率共鳴”という現象を応用したスピン波素子の研究開発があります。確率共鳴とは、通常検出できないような微弱な信号に適度な雑音(ノイズ)を加えると、ある確率の下で反応が向上し感度よく検出されるようになる現象のことです。実は多くの生き物が感覚器や神経伝達において、この確率共鳴を利用していることが知られています。例えばある種のサメの実験では、ノイズとして弱い電流を流すことによって、より遠くのプランクトンを見つけて捕食できるようになることが観測されています。 確率共鳴は私たち人間を含む多くの生体が生来備え、巧妙に活用している現象なのです。
従来の工学では、ノイズは好ましくないものとして考えられ、それをいかに除去するかに最大限の努力が費やされてきました。しかし、本研究では逆転の発想で、これまで厄介者だった“ノイズ”を有用な役割をはたす存在として活用します。研究チームは “ばらつき”や“熱ゆらぎ”などのノイズを環境中のエネルギー源と捉え、積極的に活用出来る電子デバイスの設計・開発を目指しています。

A characteristic approach of this project is the research and development of spin wave elements by applying stochastic resonance. Stochastic resonance is a phenomenon in which when an appropriate amount of noise is added to a weak signal that cannot be detected under normal circumstances, the response to the signal improves at a certain probability, and the signal is detected with high sensitivity. In fact, many creatures are known to make use of such stochastic resonance in their sensory organs and neurotransmission. For example, a fish experiment found that sharks of a certain species transmit a weak electric current as noise to find and prey on plankton farther away. Many living organisms, including human beings, naturally possess and skillfully utilize the phenomenon.
In conventional engineering, noise is considered bad and every effort is made to eliminate it. However, this project applies the reversed concept of making use of the nuisance (noise) as something useful. Our research team takes noises such as fluctuations and thermal fluctuations as energy sources in the environment and aims to design and develop electronic devices that make good use of them.
Specifically, we aim to develop a brain-type (neuromorphic) element that can function at room temperature, induce spin fluctuations on magnetic thin films made of garnet (magnetic optical material), and electrically detect spin angular momentum (spin wave) by spin-orbit interaction. These achievements will be applicable to reservoir computing by spin wave calculation, spiking neurons, etc.


Values / Hopes

本研究プロジェクトが切り開く未来の可能性 Future Possibilities Created by This Project


Machine learning is based on a learning method called neural networks that mimic the functions of the human brain as its fundamental technology. Presently, the principal artificial neural network technology is realized on software. However, in existing machines, the memory for storing programs and the CPU executing them are separated due to their structure, and heat loss is enormous in large-scale and high-speed operations with the current integrated circuit technology, resulting in the need to disregard energy efficiency.
Brain-type elements operating at room temperature, the goal of this project, can establish functions equivalent to high-density and flexible interneuron connections without wiring by applying the spin wave phenomenon and can be mounted as a chip, making them suitable for integration. We hope that this project will lead to ultra-low power consumption and high-performance terminal devices indispensable for driving the future AI society.