Embedded Artificial Intelligence
Shree L. R. Tiwari College of Engineering offers a program in Embedded Artificial Intelligence that combines the latest technologies of embedded systems, artificial intelligence, machine learning, and the internet of things. This program aims to equip students with the skills and knowledge required to design and develop intelligent systems that can function autonomously.
The program emphasizes the practical application of these technologies and their combination to create cutting-edge solutions in various fields such as industrial automation, healthcare, and space exploration. The curriculum includes courses on programming, data structures, computer architecture, embedded systems, artificial intelligence, machine learning, and IoT.
Students who complete the Embedded Artificial Intelligence program will have a deep understanding of the technicalities involved in developing intelligent systems, as well as the ability to apply their knowledge to real-world problems. This program opens up various career opportunities in fields such as robotics, automation, healthcare, and space exploration. Join the program at Shree L. R. Tiwari College of Engineering to gain expertise in the emerging area of Embedded Artificial Intelligence.
What is Embedded Artificial Intelligence?
To understand embedded AI, one needs to understand embedded systems and artificial intelligence independently and clearly.
| Embedded Systems These are isolated systems, at times standalone or a part of a larger assembly, explicitly designed to execute specific function(s) using its hardware and embedded software. | Artificial Intelligence (AI) It is the capability of a cyber-physical system controlled by a computer to carry out tasks that humans usually perform. It requires the intellectual capacity possessed by humans (human intelligence) that constitutes complex cognitive feasts and motivation and self-awareness. |
So, Embedded AI could be defined as:
Embedded AI can be defined as the capability of embedded systems or resource-constrained devices that are usually isolated to carry out tasks that require human intellectual capacity. In a more technical sense, embedded AI is the application of AI algorithms and models at the device level that can function in isolation without the need for external intervention.
What is Embedded Machine learning?
Machine learning is a sub-field or branch of AI. It is vital to understand the difference between AI and ML, as it would enable efforts in the right direction for the deployment of the most optimal solution for businesses.
Machine learning applications or ML models are resource-intensive and require a system with a lot of computing power. Hence they are usually executed on not so resource-constrained devices, for example, on a PC or cloud servers, where processing data goes smoothly. With the recent advancements in data science, algorithms, and computing power of the processors, deployment of machine learning applications or ML frameworks or directly on embedded devices is now possible. This is known as Embedded Machine Learning (E-ML) or Tiny ML applications.
Tiny Machine Learning (TinyML) is a field at the intersection of embedded IOT systems and machine learning. Embedded machine learning pushes the processing towards the edge where the sensors gather data. This helps remove barriers such as interruptions in bandwidth and connection, security breaches due to the transfer of data over the internet, and power consumption to transmit data. This is especially important for deep learning as it facilitates autonomy and intelligence at the edge. Moreover, it also facilities applications of neural networks, other ML frameworks, signal processing services, model development, gesture recognition, etc.
Worth Knowing: Current facts on Embedded AI
- The global market for Embedded AI is expected to grow at a 5.4% CAGR from 2021 to 2026, reaching about USD 38.87 billion.
- The global AI chipset market was estimated at USD 12.04 billion in 2020. The projections show that it would reach USD 125.67 billion by 2028, corresponding to a CAGR growth of 34.08% in the considered period.
- The most popular sectors are healthcare, banking and finance, automotive, manufacturing, cyber-security, smart places, and consumer electronics.
- The most popular technologies: Natural language processing, machine learning, computer vision, context-aware computing, neural networks, and TensorFlow Lite.
- The key drivers are the inclination for independent machines with self-reflection capabilities, increasing demand for more reliable and efficient intelligence solutions at the edge, and the aim to reduce human intervention.
- The key barriers are the projected reduction in the jobs, the lack of highly skilled and expert human resources in this domain area, and the skepticism of an influential few.
