Embedded Computing Design

The outcomes of embedded systems are application-oriented and advance development areas in focus, embedded systems and devices will gain more popularity in the coming future while considering various business sectors and their applications.

Even as software algorithms that mimic human thoughts and ideas are the foundation of AI, hardware is also an important component this is where the role of Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units play a vital role. 

The popularity of computer vision is growing like never before and its application is spanning across industries from consumer electronics and retail to manufacturing. With a variety of use cases including visualization, pattern recognition, segmentation, image information extraction, and classification, image processing can be done in two ways:

• Analog image processing of physical photographs, printouts, and other hard copies of images
• Digital image processing using computer algorithms to manipulate digital images

In recent years, voice technology has steadily increased in popularity, from voice control in vehicles to smart speakers in homes. A voice assistant solution is built using machine learning, NLP (Natural Language Processing), and voice recognition technology. These solutions merge cloud computing to combine AI and can converse with the end users in natural language.

Owing to revolutionary developments in computer architecture and ground-breaking advances in AI & machine learning applications, embedded systems technology is going through a transformational period.

With new-age technologies, customers now have higher expectations from their vehicles than ever before. Many are more concerned with in-car interfaces than with aesthetics or engine power. The majority of drivers desire a vehicle that makes their lives easier and supports their favorite smartphone apps.

Machine Vision has exploded in popularity in recent years, particularly in the manufacturing industry. Companies can profit from the technology's enhanced flexibility, decreased product faults, and improved overall production quality. The ability of a machine to acquire images, evaluate them, interpret (the situation), and then respond appropriately is known as Machine Vision.

Edge AI is the combination of edge computing and edge intelligence to run machine learning tasks directly on end devices. It generally consists of an in-built microprocessor and sensors, while the data processing task is completed locally and stored at the edge node end. The implementation of machine learning models in edge AI will decrease the latency rate and improve the network bandwidth.