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Published: 18/05/2022

Embedded Artificial Intelligence for Business Purposes

Embedded Artificial Intelligence for Business Purposes | DAC.digital

The fast-paced development of embedded systems and artificial intelligence (AI) in recent years has revolutionized the way we perceive the future. From industrial automation to human implants to exploring deep space, technologies such as artificial intelligence, machine learning, embedded systems (ES), internet of things (IoT) and their combinations are opening new doors to unexplored frontiers. A colossal credit for this fast-paced technological development goes to the competitive global markets, pushing businesses to innovate and expand the envelope to remain ahead of the competition.

This article will shed light on how leading technologies, such as ES, AI, and ML, are deployed in combination to obtain cutting-edge solutions and how businesses can benefit from it. 

In particular, this article will highlight the following topics:

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.

Implementation of Embedded AI (E-AI) system | DAC.digital

Origins of Embedded AI

AI and embedded systems (ES) have been around for a long while now. However, their success curves have been quite different. Over the last quarter of the 20th century and the early part of the first decade of the 21st century, AI failed to realize the early promise it had shown. Its application and usability retreated to highly specialized niches. This was mainly due to the lack of low cost and high volume manufacturing of required electronic hardware components, the high bandwidth needed to feed big data to the AI algorithms, and the resources required to process the data. There was also a lack of data scientists and engineers who were specialists in this field. On the other hand, applications of ES technology steadily grew and then took off in the 21st century. Today, AI and ES are among the technologies experiencing an innovation surge.

Let’s talk a little philosophy!

Many considered Plato the most significant philosopher who lived; he famously wrote that “our need will be the real creator,” interpreted as the proverb “necessity is the mother of invention.”  Recent research proves this, yet again, this time in terms of innovation drivers. The human race is facing many challenges presently, and for businesses, the competition in the global markets has never been more challenging. Creating new knowledge, applying innovation, and using cutting-edge technology in business solutions have become the norm. The worldwide rise of the demand for embedded systems and growing applications of artificial intelligence, along with the unprecedented pace at which they are projected to grow in the coming years, has enabled a transformation towards merging AI and ES and created a promising new sector of Embedded AI. The significant drivers of Embedded AI are increased functionality and responsiveness, reduced transfer of training data, and increased privacy, security, and resilience. There is still a void for development engineers and scientists to optimally deliver these benefits—a recommended career choice for those planning their professional journey.

Which is better, embedded or AI?

It’s not fair to compare embedded and AI. Oh, then why do we ask which is better? The humorous answer would be ‘to catch your attention,’ but the actual answer would be to distinguish the two and highlight their support for each other.

An AI model learns from the available data, making better decisions. At the same time, embedded systems are the physical devices that can generate the information or data through sensors that can be fed to the AI algorithms. The more trained models, the better the outcome. Hence, Embedded AI is a powerful solution, especially for constrained devices.

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 TinyML applications.

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.

Embedded AI Applications for Businesses

Let’s get down to business now, literally. The purpose of any technology is to contribute to the growth of business and/or society. This is what makes technologies successful. The same goes for Embedded AI or Embedded Machine Learning.

Embedded Intelligence | DAC.digital

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.

Why should you integrate Embedded AI into your business?

For businesses, Embedded AI or Edge AI holds several advantages compared to conventional solutions in any industry. Below we highlight a few of them (P.S. This is not an exhaustive list).

Economics

For example, conventional solutions based on cloud technology are getting more economical but still cost a lot of money. Apart from the costs involved for additional operations once the data reaches the cloud, significant expenses are also incurred in data transfer from the device to the cloud. Deployment of Embedded AI solutions eliminates the need for communication with the cloud, as the device can process the data and has enough computing power to train the AI models. Hence reducing the costs significantly.

Bandwidth

AI algorithms require a large amount of data for the analysis and training of the models. A large bandwidth is necessary to transfer the data for computing to the cloud or other data centers. With Edge AI or Embedded AI, devices become independent, and there could be little or even no bandwidth available for any device to perform its function.

Privacy

Sensors and recording devices at the edge generate sensitive data and pose privacy concerns. Sharing this sensitive data over the multiple layers of the internet increases the risk of a privacy breach. Processing the data at the edge and avoiding data transmission significantly lowers the probability of a violation. Thus strengthening the privacy control of the device.

Latency

Embedded AI deployment significantly decreases the latency of the systems. This is due to the sensor data being processed locally instead of being transferred to a secondary location for computing. This is very important for real-world applications and services requiring real-time solutions with AI. Be it swift reactions autonomous vehicles need when encountering an obstacle or a system response to signal processing, the ability to respond quickly is vital.

Reliability

A device that can locally process data is less prone to faults and reduces downtime. This is an utmost requirement for specialized tools in the industry or sensitive devices on which users are heavily dependent. Embedded AI solutions are far better in this aspect than convectional AI computing solutions.

How can you integrate Embedded AI into your business?

The simplest way would be to get in touch with us, and one of our experts will go over the details of development with you step by step. It has taken years of R&D for us to develop optimal solutions and procedures that we employ to enable our clients and facilitate their business requirements.

From the phase of need analysis to development to full deployment, you would be able to benefit from our four key elements of cooperation: Workshops of discovery, user-experience design at the interface, solid ground of software architecture, and custom solutions crafted especially for your business. Whether hardware or software, ML models or other frameworks, embedded or other devices, neural network,s or deep learning, our services offer a wide range of emerging tech solutions to help your business grow.

Dr. Yash Chawla

Content Marketing Advisor, DAC.digital | Asst. Professor, Wroclaw Tech.

Yash is a researcher, academician, and consultant in the field of marketing and innovation management, with keen interest in marketing communications, creative thinking, and sustianable development. He strives to make a positive difference for students, professionals and the society.

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