- TDK’s real-time analog chip learns at the edge for robotics and sensors
- Demo shows high-speed learning in a rock-paper-scissors challenge
- Neuromorphic approach aims to merge sensing and AI for edge computing
To most people, TDK is best known for its audio cassettes, which were a staple of home recordings and personal music collections throughout the 1980s and 1990s.
Although once synonymous with blank tapes and magnetic materials, the company has since become a major developer of advanced electronics and sensor technologies.
Now, TDK, in collaboration with Hokkaido University, has developed a prototype analog tank AI chip that it says is capable of real-time learning.
rock-paper-scissors
The technology mimics the human cerebellum and processes time-varying data at high speed and ultra-low power, making it suitable for robotics and human-machine interfaces.
By learning directly at the edge and using analog circuits for reservoir calculation, it differs from traditional deep learning models that rely on cloud processing and large datasets.
Silicon uses the natural physical dynamics of analog signals, such as wave propagation, to interpret, capture and produce output efficiently with minimal consumption.
According to TDK, the prototype’s ability to learn in real time will allow it to quickly adapt to changing data streams, making it ideally suited for uses requiring instant feedback, such as wearable devices, autonomous systems and IoT hardware.
The company will showcase the prototype at the upcoming CEATEC 2025 event in Japan, where a demonstration device will challenge visitors to a game of rock-paper-scissors using acceleration sensors to track hand movement and predict the winning gesture before the player has a chance to complete their move.
“In the game rock-paper-scissors, there are individual differences in finger movement, and in order to accurately determine what to do next, it is necessary to learn these individual differences in real time,” TDK explained.
“This demonstration device is attached to users’ hands, the movement of the fingers is measured using an acceleration sensor, and the simple task of deciding what to play with rock-paper-scissors is processed in real time and at high speed on the AI chip of the analog tank, allowing users to achieve “stone-paper-scissors that can never be won.”
The company said it hoped the demonstration of the prototype chip would “promote a broader understanding of tank computing” and that this would lead to accelerated commercialization of tank computing devices for cutting-edge AI applications.
The new design builds on TDK’s previous research into neuromorphic devices that attempted to mimic the brain using spintronics.
Instead of tackling heavy computation tasks, this analog tank AI is designed for fast, low-power processing of time series data, making it perfect for edge sensing and control.
TDK announces plans to expand its collaboration with Hokkaido University and apply the results to its Sensor Systems business and its TDK SensEI brand.
Via eeNews analog
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