Issue link: https://resources.mouser.com/i/1499313
Renesas 2023 9 In applications where power consumption at the endpoint is limited, microcontrollers (MCUs) or microprocessors (MPUs) need to be more efficient to take on the high volumes of multiply-accumulate (MAC) operations that are required for AI processing. Deployment of AI Vision Applications There are unlimited use cases for the deployment of AI in vision applications in the real world. Here are some of the examples where Renesas Electronics can provide comprehensive MCU- and MPU-based solutions, including all the necessary software and tools to enable quick development. Smart Access Control Security access control systems are becoming more valuable with the addition of voice and facial recognition features. Real-time recognition requires embedded systems with very high computational capabilities and on-chip hardware acceleration. To meet this challenge, Renesas provides a choice of MCU or MPU that offers very high computational power that also integrates many key features that are critical to high-performance facial and voice recognition systems, such as built-in H.265 hardware decoding, 2D/3D graphic acceleration, and error correction code (ECC) on internal and external memory to eliminate soft errors and allow for high-speed video processing. Industrial Control Embedded vision has a huge impact as it enhances many applications, including product safety, automation, and product sorting. AI techniques can perform multiple operations in the production process (such as packaging and distribution), which can ensure quality and safety during production in all stages. Safety is needed in areas such as critical infrastructure, warehouses, production plants, and buildings that require a high level of human resources. Transportation Computer vision presents a large scale of ways to improve transportation services. For example, in self- driving cars computer vision is used to detect and classify objects on the road. It is also used to create 3D maps and estimate movement around. By using computer vision, self-driving cars gather information from the environment using cameras and sensors, which then interpret and analyze the data to make the most suitable response by using vision techniques such as pattern recognition, feature extraction, and object tracking (Figure 1). In general, embedded vision can serve many purposes, and these functionalities can be used after customization and the needed training on different types of datasets from many areas. Functionalities include monitoring physical area, recognizing intrusion, detecting crowd density, and counting humans or objects or animals. They also include identifying people or finding cars based on license plate numbers, detecting motion, and analyzing human behavior in different cases. Figure 1: Computer vision is used to detect and classify objects on the road. (Source: Renesas Electronics) Embedded vision is one of the leading technologies, with embedded AI used in smart endpoint applications in a wide range of consumer and industrial applications. " "