What is Edge AI?

by Stephen M. Walker II, Co-Founder / CEO

What is Edge AI?

Edge AI, or Edge Artificial Intelligence, is the implementation of artificial intelligence in an edge computing environment. This means that AI computations are performed close to where data is collected, rather than in a centralized cloud computing facility or an offsite data center.

Edge AI allows devices to make decisions quickly, often in milliseconds, without needing to connect to the cloud or offsite data centers. This is achieved by running AI models on processors inside the connected devices operating at the network edge, adding a layer of intelligence where the device not only collects data but also processes it.

The benefits of Edge AI include reduced latency, improved privacy, and more efficient use of network bandwidth. It also enhances data security as sensitive data never actually leaves the edge. Furthermore, Edge AI can help reduce the cost of internet bandwidth and cloud storage, and it can improve the reliability of systems.

Edge AI has a wide range of applications. Notable examples include facial recognition, real-time traffic updates on semi-autonomous vehicles, connected devices, and smartphones. It's also used in video games, robots, smart speakers, drones, wearable health monitoring devices, and security cameras.

In the industrial sector, Edge AI can be used to automate assembly lines and visually inspect products for defects. It can also monitor potential defects and errors in the production chain and enable real-time adjustments to production processes.

Edge AI is a powerful technology that brings AI closer to the data source, enhancing responsiveness, boosting security and privacy, and offering real-time decision-making capabilities. It's a key driver in the era of the Internet of Things (IoT), enabling a new generation of smart, autonomous devices and systems.

How can I run Edge AI locally?

Running Edge AI locally involves deploying AI models and algorithms on edge devices or local servers, rather than relying on cloud-based processing. This approach brings AI capabilities to where data is generated, resulting in faster and more efficient processing, real-time analysis, and reduced dependence on internet connectivity. Here are some techniques and considerations for running Edge AI locally:

  1. Hardware Selection — Choose the right edge device for your AI application, considering its hardware specifications, power consumption, connectivity, and security. Companies like Apple, Google, and NVIDIA are creating AI chips to enhance on-device processing.

  2. Model Optimization — Design your AI model to fit the edge device constraints through techniques such as quantization, pruning, distillation, or neural architecture search. Some popular AI algorithms used in Edge AI systems include convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequence data processing, and reinforcement learning algorithms for decision-making tasks.

  3. On-Device Training — Techniques like PockEngine, developed by researchers from MIT and elsewhere, enable deep-learning models to efficiently adapt to new data on edge devices. PockEngine determines which parts of a machine-learning model need to be updated to improve accuracy, and only stores and computes with those specific pieces.

  4. Deployment and Maintenance — Deploy the AI model to the edge devices and automate monitoring mechanisms to track and collect data on the resource performance, and potential issues of the AI models on the devices. Collect user feedback, data, and performance metrics. Conduct routine updates and maintenance and continually enhance the edge AI solution.

  5. Use of Edge AI Frameworks — These frameworks often come with development tools, pretrained models, and cloud-native suites of AI and data analytics software, making it easier to train and update AI models on edge devices.

  6. Edge AI Software Platforms — Companies are developing software platforms specifically designed for edge AI. For instance, Google's TensorFlow Lite and Facebook's PyTorch Mobile cater to on-device AI.

Remember, the deployment of AI models on edge devices is a complex process that requires careful planning and execution. It's important to test and validate your AI model on edge devices using realistic data and latency, and resource consumption metrics.

How does Edge AI differ from Cloud AI?

Edge AI and Cloud AI represent two distinct paradigms for deploying artificial intelligence systems, with key differences in data processing location, latency, connectivity, and scalability.

Data Processing Location

  • Cloud AI processes and stores data on cloud platforms or servers, where AI algorithms and models are executed.
  • Edge AI captures or receives data and runs AI algorithms and models on local devices such as wearables, IoT devices, or edge computing servers.


  • Edge AI provides lower latency because data processing occurs locally, allowing for near-instantaneous decision-making.
  • Cloud AI may introduce latency due to the time taken for data to travel to the cloud for processing and back to the device.


  • Edge AI can function effectively even in environments with poor or no connectivity, as it does not rely on a constant connection to the cloud.
  • Cloud AI requires internet connectivity to function, which can be a limitation in areas with unreliable network coverage.

Scalability and Cost

  • Edge AI may have higher initial costs due to the need for more powerful local hardware but can save on cloud communication costs over time.
  • Cloud AI can be more cost-effective for applications that do not require real-time processing and can leverage the cloud's scalability.

Security and Privacy

  • Edge AI can offer improved security and privacy since sensitive data can be processed locally without being transmitted to the cloud.
  • Cloud AI requires data to be sent to and from the cloud, which can raise concerns about data security and privacy.

Use Cases

  • Edge AI is suitable for real-time applications, such as industrial automation, healthcare monitoring, and autonomous vehicles, where immediate processing is critical.
  • Cloud AI is better suited for applications that can tolerate some latency and benefit from the cloud's vast computational resources, like large-scale data analysis and training complex AI models.

In practice, Edge AI and Cloud AI can be complementary, with edge devices handling real-time processing and the cloud providing deeper insights and model training capabilities. The choice between Edge AI and Cloud AI will depend on the specific requirements of the application, including the need for real-time processing, connectivity, scalability, and data sensitivity.

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