e-AI Solution
What is Renesas' e-AI Solution?
Anyone can use AI (Artificial Intelligence) relatively easily by using Caffe developed by UC Berkeley or TensorFlow developed by Google.
e-AI Demonstration Experiment
A demonstration experiment on equipment abnormality detection and predictive maintenance by e-AI was conducted (2015) at the semiconductor front-end factory -Renesas Naka factory.
e-AI Solution Use Cases
We are exploring new e-AI solutions for customers in Renesas' focus segments of smart factory, smart home, smart infrastructure, and a new business segment, service robots. We will continue to cooperate with customers and partners going forward.
Neural Network Structures Convertible by Translator
The translator (free version) supports microcontrollers with comparatively small ROM/RAM capacity. In order to compress the capacity used by the library, only functions that are often used by neural networks are supported.
Translator Tutorial
This tutorial explains how to use the translator using a well-known neural network as an example.
Development Environment & Downloads
Tools for running learned AI on Renesas MCUs and MPUs
Links & Contact Information
Inquiries and information, Development Environment/Supported Products, and other links.
The evolution of artificial intelligence (AI) technologies such as machine learning and deep learning has been remarkable in recent years, and the range of application is rapidly expanding from the cloud market mainly focused on the IT field to the embedded system market. For example, service robots.
Embedded devices equipped with AI may become necessary in future for service robots that need to perform judgment and control according to various situations. In addition, it is expected that the development of embedded devices equipped with AI will accelerate not only for service robots but also for services and their associated devices in general that require interaction with people. Under these circumstances, Renesas "e-AI" implements artificial intelligence technology in embedded devices.
| 1. e-AI translator | Converts the learned AI network of Caffe or TensorFlow, open source machine learning / deep learning frameworks, to the MCU/MPU development environment. |
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| 2. e-AI checker | Based on the output result from the translator, the ROM/RAM mounting size and the inference execution processing time are calculated while referring to the information of the selected MCU/MPU. |
