On-device learning AI chip targets edge IoT apps

Rohm Semiconductor has developed an on-device learning AI chip (SoC with on-device learning AI accelerator) for edge computer endpoints in the IoT field. The new AI chip uses artificial intelligence to predict failures (predictive failure detection) in electronic devices equipped with motors and sensors in real time with ultra-low power consumption.

Generally, AI chips perform learning and inferences to achieve artificial intelligence functions, as learning requires a large amount of data to be captured, compiled into a database, and updated as needed. So, the AI chip that performs learning requires substantial computing power and consumes a large amount of power. Until now, it has been di cult to develop AI chips for edge computers and endpoints that can learn in the field and consume low power to build an e cient IoT ecosystem.

Based on an “on-device learning algorithm” developed by Professor Matsutani of Keio University, Rohm’s newly developed AI chip mainly consists of an AI accelerator (AI-dedicated hardware circuit) and Rohm’s high-e ciency 8-bit CPU “tinyMicon MatisseCORE.” Combining the 20,000-gate ultra-compact AI accelerator with a high-performance CPU enables learning and inference with an ultra-low power consumption of just a few tens of milliwatts (1000 times smaller than conventional AI chips capable of learning). 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.

Going forward, Rohm 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 for 2024. DW

Rohm | rohm.com