Embedded AI and edge computing are transforming how industries operate by enabling devices to process data locally. This approach drastically reduces latency and makes real-time decisions possible in applications where every millisecond counts.
Key Takeaways:
- Embedded AI brings intelligence close to data, meaning real-time, low-latency decisions are possible when needed.
- Training teams for edge AI needs hands-on experience with specialized hardware and performance optimization techniques.
- Low-latency applications power devices such as autonomous vehicles, smart factories and real-time health monitoring systems.
- Continuous learning of dynamic up-and-coming technology such as TinyML, 5G, and federated learning sticks enables organizations to stay competitive.
- Providing the appropriate training courses for your employees will ensure teams
Understanding Embedded AI and Edge Computing
What Is Embedded AI?
Embedded AI refers to the use of artificial intelligence in small, targeted devices that stand alone. These devices process data on the edge and don’t rely on the cloud for processing. The local processing of the data decreases the latency substantially.
For instance, a factory sensor that finds problems and alerts immediately without waiting for confirmation from the cloud. Such immediate response not only saves money, but can save lives in many different applications.
Why Low-Latency Matters
Latency is the time gap between when data is detected and an action takes place. The action needs to occur closely after the data is detected, particularly in low-latency AI application areas, where that time gap has to be very small. Consider self-driving cars; they have to respond without delay to avoid a potential hazard. If there is delay, accidents can easily occur.
When it comes to split-second decision-making in autonomous vehicles, actions taken by health monitors – when they send an alert signal immediately, catching faults by industrial machines in the moment, or security camera threat detections that need to be sensed and reported in real-time, low latency becomes very important .
Embedded AI processing works good in all cases where it operates locally.
The Growing Demand for Edge AI Training
Why Train Your Team on Embedded AI?
As the market for embedded AI devices grows rapidly, companies have a need for engineers who are trained in hardware and software and fit for the design of tight, low-latency systems. This presents a tremendous challenge due to limited resources in edge devices (i.e., less power and less memory), since the models must be optimized incredibly carefully.
Training teams is critical to give them the skills needed to develop efficient embedded AI systems designed with the expectation that they will run very quickly and very safely.
What Skills and Tools Does Your Team Need?
Provide training for your employees on hardware accelerators (ARM CPUs, FPGA, GPUs), artificial intelligence frameworks including TensorFlow Lite, OpenVINO, and Edge Impulse, model optimization techniques such as pruning, quantization, and transfer learning, and networking technologies including 5G, Bluetooth LE, and LPWAN.
There are a number of well-regarded frameworks and platforms for accelerating training and development including TensorFlow Lite for working with lightweight models and OpenVINO for Intel hardware.
Fundamentals of Low-Latency AI Applications
Understanding Latency Causes
Latency is primarily due to data going back-and-forth, agile human.
By using edge AI, all data either stays local or close to the device, pre-trained models are optimized for speed at no likelihood committee of losing accuracy, and the computation time is not usually measured in seconds, but in milliseconds, using specialized chips.
Examples of meaningful reductions in latencies are in daily use including wearable activity trackers with latencies area close to 1.5 msecs. This achieves almost instant and reliable sensing.
Common Use Cases for Low Latency
Examples are drones that fly in real time, with onboard AI vision, smart factories spotting defects immediately in the manufacturing process to avoid downtime, security cameras reviewing video with eyewitness capabilities for threats immediately, and medical devices notifying caregivers in emergencies.
Technologies Powering Embedded AI on the Edge
Essential Hardware
Common hardware is made up of FPGAs (Field Programmable Gate Arrays) which are programmable chips that accelerate the process of AI, ARM CPUs (central processing unit), which are efficient processors in smartphones and IoT, and Neural processing units (NUPs) which are specifically designed for AI to perform tasks such as matrix algebra.
All of these allow AI to benefit from speed while being efficient with battery overheat.
Software & Connectivity
There are a number of important software packages. TensorFlow Lite can run AI models on mobile and edge devices, OpenVINO can speed up AI using Intel chips and Edge Impulse can quickly put AI prototypes into production.
Connectivity technologies like 5G and Bluetooth Low Energy are now capable of continuously streaming data with good performance and reliability.
FPGA can give fast and adaptable processing, ARM CPUs are power efficient, TensorFlow Lite helps optimize AI deployment in the real world, and 5G can provide a continuous stream for real-time data. Together, these technologies provide a strong basis for AI embedded solutions.
Training Your Team: Best Practices
Create a Hands-On Curriculum
Good training consists of the fundamentals of edge AI and real-time principles, hardware platforms and AI frameworks, simulations from real world projects, model size and speed improvements (pruning, quantization), and security and privacy protection of data on edge devices.
Continuous Learning is Vital
Technology is always evolving. Encourage your team to use online courses and relevant webinars, experiment with new tools and frameworks, and keep up to date with federated learning, 6G, and TinyML
Tools for Practical Training
Simulators and dev kits allow your teams to train at a lower cost, while cloud to edge constraints will offer real-time testing of models.
