Artificial Intelligence and Machine Learning in Embedded Systems.
Globally as enterprises generate/gather data at an aggressive pace, businesses are focusing on effective ways to analyze the data and generate insights that will help them improve their business process and make sound decisions. Artificial intelligence (AI) and Machine learning (ML) are addressing this need and solving complex problems, automating processes, and enabling self-learning from experience.
ML, a widely used subset of AI technology, helps to leverage huge amounts of historic data that will help to build machines that can learn and improve themselves by experience, just like humans improve themselves through learning as well as observation. For the past decade, most ML has revolved around Deep learning (DL), a segment of Machine learning which focuses on Deep Neural Networks (DNNs). As Artificial Intelligence and deep learning applications are data-intensive, they are associated with Embedded industry practitioners and domain experts by using the computing resources such as graphics processing units and other related processors.
Executing Machine learning models on any embedded devices is known as Embedded Machine learning. Now let's discuss its importance and impact on businesses.
Training and Operations – Embedded machine learning operates based on the principle that Machine learning models such as neural networks can be trained on the cloud or computing clusters, and operations and execution of the models takes place only on the embedded devices.
Embedded Machine learning capabilities – Embedded machine learning helps to unveil the capabilities of processing data within the hundreds of billions of embedded controls that are available in various types of settings such as smart buildings and residential environments, manufacturing floors, industrial plants, etc. Furthermore, it also enables the processing of the data that are produced by several embedded devices that are related to the Internet of Things.
Benefits of Machine learning models on embedded devices – There are several benefits regarding the execution of Machine learning models on embedded devices when compared to the conventional cloud-based AI. Benefits such as reduced power consumption, network bandwidth efficiency, privacy, improved environmental performance, and low latency.
To make useful predictions Artificial intelligence(AI) and Machine learning is an effective method for building models that use the data, for instance, IoT sensors data. For embedded systems, executing ML for models and bringing AI to the sensor point where there is a high possibility of data generation is a challenge.
Valuable Data – As the microcontroller units are in every industrial and consumer device, it is a challenge to include a Machine learning model into the power and memory-constrained hardware. Machine learning will help to unleash the valuable use of 99% of sensor data which is ignored due to the elements of the cost, power, or bandwidth constraints.
On device-AI – On device-AI helps to connect devices that include smartphones, wearables, automobiles cameras, and Industrial IoT sensors, programmatic logic controllers (PLCs), and edge computing services or gateways. With On-device AI, for example, robotics are used for innovative purposes, healthcare solutions, industrial production, and predictive maintenance.
Embedded Machine learning ecosystem – Based on the tools and techniques, embedded Machine learning applications run on different types of embedded devices which enables the development and deployment of machine learning models. An ideal embedded Machine learning ecosystem includes device vendors, the Original equipment manufacturers(OEMs) where models regarding Machine learning are employed and executed. Furthermore, it extends the Machine learning ecosystems with techniques and tools for developing and deploying embedded devices including the Internet of Things(IoT) which is popularly known as Artificial Intelligence on IoT devices (AIoT).
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