Edge AI For Autonomous Operations

What is Edge AI? 

Edge AI is a combination of Edge Computing and Artificial Intelligence. Edge computing is based on the general idea that data is created, stored, collected, processed, and managed at a local location rather than at a data center. Edge AI takes this idea to the device level. It uses machine learning, or ML, which mimics human thinking, to reach the point where a user interacts with a machine, edge server, or IoT device. 

Importance of Edge AI for Autonomous Systems

Edge AI provides several advantages for autonomous systems, such as: 

• Reduce latency and response times: Edge AI processes data on edge devices, eliminating the need for data to be sent to the cloud to be processed. This reduces latency and allows autonomous systems to respond more quickly to environmental changes. 

• Reduce privacy and security risks: Edge AI stores data locally at the edge device, reducing the chance of data breaches and unauthorized access. 

• Reduce bandwidth requirements: By using Edge AI, autonomous systems can reduce the amount of data they need to transmit over the network, resulting in lower bandwidth costs and improved network performance. 

• Decreased network outages and fault tolerance: An Edge AI system is less likely to fail due to network outages or other disruptions, as it can continue to operate even if a connection to the cloud is lost. 

Components of Edge AI for Autonomous Operations 

Edge Devices 

• Edge devices are physical devices that power the Edge AI model. They typically have low processing power and limited memory but are small, low-power, and inexpensive. Common edge devices include embedded systems, microcontrollers, and FPGAs. 

• Edge sensors collect environmental data, such as temperatures, pressures, vibrations, images, and more. They can be integrated directly into edge devices or connected to them via wired and wireless interfaces. 

AI Algorithms and Models 

• Edge AI models typically use machine learning (ML) or deep learning (DL) algorithms to learn from data and make predictions or decisions. 

• Deep learning algorithms are a subset of machine learning, which uses artificial neural networks (ANNs) to learn complex patterns from data. 

• After an edge device has been trained, an AI model can be deployed to it. The edge device then uses the model to process the data and make real-time decisions. 

Edge device connectivity and communication 

• Edge devices are typically connected to the cloud or another network via Wi-Fi, Bluetooth, or cellular networks. 

Key Considerations for Implementing Edge AI

Data collection and preparation 

• Data collection and preparation for Edge AI requires high-quality data. To train effective AI models, you need to collect data that is relevant to the task the AI model will perform. 

• Data must be accurate and free from errors. 

• Data diversity must be representative of a wide range of situations and scenarios. 

• Once you have collected your data, you need to prepare it for training. This may include cleaning, preprocessing, and extracting features from your data. 

Model selection and training  

• The selection of an AI algorithm and model is based on the specific job that the autonomous system will perform. Once the algorithm and model are chosen, the model can be trained using the pre-trained data.  

What is model training?  

• Model training is the iterative process of fine-tuning the hyperparameters of the model and evaluating the model’s performance. The aim of model training is for the model to be able to generalize to new data and to be able to perform well on the task at hand.  

Deploy and Optimize  

• After training an AI model, you can deploy it to your edge device. But before you can deploy your model, you need to optimize it for efficiency and performance. You can do this by using techniques like model compression, model quantization, or pruning.