Embedded systems play an important role in many industries. They perform control, regulation and information-processing tasks. They can be stand-alone or network-embedded.
A new trend in embedded development involves monitoring and improving technical equipment. This can lead to lower rates of defects and increased safety.
Other trends include optimizing battery-powered devices for minimal power consumption and improved connectivity. These developments are driving innovation in the field of embedded systems.
As their name suggests, embedded systems are the core of smart devices and products. From home light systems that turn on and off base on human presence to digital solutions in retail or condition monitor for industrial machines, embedd AI technologies like machine learning algorithms are increasingly use by companies to make their products more intelligent and improve functionality.
In addition to machine learning, embedded AI is also enabling smarter automation solutions by tapping into large-scale data. This technology, often called “big data,” enables the real-time analysis of complex and heterogeneous information to provide insights that can be acted upon to create value and mitigate risk.
As the number of IoT devices continues to grow, so too does the need for intelligent, energy-efficient embedded systems. Embedded systems designed for edge computing bring processing and storage closer to the devices that use them, reducing latency and improving reliability. These systems are also able to operate independently of cloud servers, making them energy-efficient and safeguarding data sovereignty.
Edge AI is a type of machine learning that analyzes data directly at the source rather than in the cloud, eliminating the need for long-distance communication. This allows for quicker processing, lower latency and tighter security.
For example, a grocery store can use edge AI to analyze video data and determine that it needs to restock oranges around noon every day. This information can help optimize the stocking of products, improve customer service, and spark new business models.
Edge AI is especially vital for smart city infrastructure, such as a robust healthcare monitoring system that can identify the presence of a virus or chronic disease and alert doctors to it in real-time. It can also enable autonomous systems to detect anomalies in their environment and take appropriate action. Additionally, it can provide faster processing speeds for IoT devices that rely on large amounts of data and require high-quality video output, such as surveillance cameras or medical equipment.
Neuromorphic Hardware Architectures
Several research efforts are currently underway to develop neuromorphic hardware architectures to achieve low power computing. These architectures are inspired by the human brain and can be used in edge AI applications for tasks such as pattern recognition, fault detection and behavioural analysis.
Visitors to the embedded world exhibition will be able to see how these technologies can be applied in real-life systems through a number of demonstrations. For example, Fraunhofer IIS will showcase a reliable augmented reality system that uses low-power OLED microdisplays.
Another demo will show how complex general-purpose compute tasks can be broken down into simple functions that are easily implemented on a neuromorphic hardware platform. For instance, the virtual neuron abstraction used to encode numbers like (sqrt2) or e as a vector with a given bit length. This enables much faster application development and reduces the communication load on the network. A live demonstration of a CerebelluMorphic system base on Altera Stratix V FPGAs will also shown to demonstrate the effectiveness of this approach.
Artificial Intelligence (AI)
With consumers and businesses generating data at an exponential rate, AI is increasingly becoming the gold standard for automating business processes and making informed decisions. AI technologies such as machine learning and deep learning leverage large amounts of historic data to build machines that learn, improving themselves over time, much like humans do.
Embedded AI systems are hardware-software systems that perform dedicated functions within larger systems such as autonomous vehicles or smart home devices. These systems can benefit from the added intelligence offered by embedded machine learning, enabling advanced features such as predictive maintenance or anomaly detection.
However, executing AI algorithms on embedded systems requires significant processing power that raises energy sustainability concerns. A solution is edge AI, which brings computation and storage closer to the source of the data, reducing the need to transfer large amounts of data to remote locations for analysis. This also reduces latency, allowing real-time data analytics and intelligent operations.