Ultra-low-power on-device-learning edge AI chip
Electronic components manufacturer ROHM Semiconductor has announced that it has developed an on-device learning AI chip (i.e., an SoC with on-device learning AI accelerator) for edge computer endpoints in the IoT field. The device utilizes artificial intelligence to predict failures (predictive failure detection) in electronic devices equipped with motors and sensors in real-time with ultra-low power consumption.
Until now, says the company, due to the computing power required for AI learning, it has been difficult to develop AI chips that can learn in the field consuming low power for edge computers and endpoints to build an efficient IoT ecosystem.
The newly developed AI chip mainly consists of an AI accelerator (AI-dedicated hardware circuit) and the company’s high-efficiency 8-bit CPU tinyMicon MatisseCORE. Combining the 20,000-gate ultra-compact AI accelerator with a high-performance CPU enables learning and inference with ultra-low power consumption of just a few tens of mW (1000× smaller than conventional AI chips capable of learning), says the company. This allows real-time failure prediction in a wide range of applications, since anomaly detection results (anomaly score) can be output numerically for unknown input data at the site where equipment is installed without involving a cloud server.
For evaluating the AI chip, the company offers an evaluation board equipped with Arduino-compatible terminals that can be fitted with an expansion sensor board for connecting to an MCU (Arduino). Wireless communication modules (Wi-Fi and Bluetooth) along with 64 kbit EEPROM memory are mounted on the board, and by connecting units such as sensors and attaching them to the target equipment it will be possible to verify the effects of the AI chip from a display. The evaluation board will be loaned out from ROHM sales.
Going forward, the company says that it plans to incorporate the AI accelerator used in this AI chip into various IC products for motors and sensors. Commercialization is scheduled to start in 2023, with mass production planned in 2024.
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