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A day will come, said ST, when a network of tiny MCUs is smart enough to detect wear and tear in machines on the factory floor or find anomalies in a building, without necessarily reporting sensor readings back to data centers.Īt that time, ST demonstrated three AI solutions: a neural network converter and code generator called STM32 CubeMX.AI, ST’s own Deep Learning SoC (codenamed Orlando V1), and a neural network hardware accelerator (currently under development using an FPGA) that can be eventually integrated into the STM32 microcontroller.įully aware of the competitive landscape, Renesas’ Baba spoke confidently. Earlier this year during the Mobile World Congress, STMicroelectronics presenters described machine learning as a key to “distributed intelligence” in the embedded world. The concept of embedded AI is expected to spread throughout the industry. The company’s e-AI roadmap enhanced by DRP covers a broad range of AI processing, starting with real-time image processing (scheduled to launch in October), real-time cognition (2020) and endpoint incremental learning (2022). Renesas plans to integrate DRP as a core inside an MCU. Renesas is announcing next month what the company calls DRP - a dynamically reconfigurable processor - as an AI accelerator that works in tandem with its own MCU.ĭRP enables two dynamic reconfiguration capabilities (Source: Renesas) Renesas is also cognizant that endpoint inference on MCU/MPU will eventually need more complex AI processing, prompting customers to demand a roadmap for enhanced e-AI.
Renesas dominates factory floors, building infrastructure and home appliances with its microcontrollers, SoCs and microprocessors. Pitching MCU-based “embedded AI” (or “e-AI”) is a smart move. “Our goal is to lead a new market segment of embedded AI, in which data required for inference is so small that it can even run on existing MCU/MPU,” Baba said. Rather than replacing existing production lines with brand-new AI enabled machines, which would be costly, Renesas is proposing an “AI Unit Solution” kit that can be attached to current production equipment.īaba said Renesas has no plans to challenge AI chip companies like Nvidia. In short, Renesas is advocating AI inference that can be done on an MCU. Baba said 30Kbytes of data is usually enough for end-point inference, compared to statistical AI doing both learning and inference, which typically demands processing data as big as 300 megabytes in the cloud. AI inference runs on endpoint devices in real time, without returning to the cloud. In such a factory automation example, AI needs to be trained only once based on pre-identified issues. AI could be the watchful eye monitoring the production line continuously, to keep small products defects from advancing to the next stage of production. Mitsuo Baba, senior director of the strategy and planning division of Renesas’ Industrial Solution business unit, told us that AI can be best applied to OT when specific issues - in production lines for example - are already identified.įor example, suppose there is a highly skilled operational manager who is experienced enough to detect certain anomalies in a factory. Instead of sending this manager to check out every stage of the manufacturing process, “We could use AI to draw the line - and define - when and where an abnormal situation begins to emerge during the production defects,” said Baba. (Photo: EE Times)īy bringing AI in baby steps to factory floors, Renesas hopes to help customers currently struggling to complete the proof of concept on their own AI implementation and understand their return of investment in AI. Survey: Please let us know how you rate this product an if you have suggestions by taking this quick survey.Yoshikazu Yokota, executive vice president at Renesas, plans to focus on offering real-time inference in OT. Nonmembers will need to go directly to Wiley to access INCOSE Symposia papers for a fee:
Gehc learning factory for inferencing download#
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Gehc learning factory for inferencing professional#
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