イベント名
Distinguished Lecturer Program Series
発表年月日
2022/04/26
タイトル
Physical Reservoir Computing: Its Advantages and Significance
講演者
Akira Hirose(The University of Tokyo), 
抄録
This talk focuses on reservoir computing and its physical realization in the AI & sensor-network era. First, we have a glance of the hardware history in artificial intelligence (AI) including logic architecture and neural networks. The key point in modern AI is emphasized, namely, pattern information representation and pattern information processing. We also present the basic idea and dynamics of reservoir computing by referring to deep learning and time-serial information processing.
Then we look through various physical reservoir-computing ideas briefly to catch the variety of physics used in this area and also the key properties. In particular, we go into the details of reservoirs based on photonics and spin waves. The former has been studied for long, resulting in an accumulation of experiences. The latter is new and rich in nonlinearity, hysteresis, anisotropy and dispersion, which enhance the processing ability.

Lastly, we discuss the prospects of reservoir computing in the near future society in the context of edge-computing as well as server use in the sensor-network society.
【CONTENTS】
1. A glance of AI and neural hardware history
1.1 Brain exploration, logic-computer development and neural-network investigation
1.2 Various logic and neural chips
1.3 Symbol information representation/processing and pattern information representation/processing

2. Reservoir computing
2.1 Aims and merits
2.2 Theoretical aspects
2.3 Expected practical applications

3. Physical reservoir computing
3.1 Direct physical realization of reservoir computing hardware
3.2 Optical reservoirs
3.3 Spin-wave reservoir chips

4. Near future applications and scientific significance

5. Future prospects

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