What is Edge Computing? 

Edge computing is a type of computing that bring processing power and data storage closer to the source where the data is generated. Typically, edge computer systems can process data locally without ever needing to connect to the internet. This is so because data is processed locally, allowing the edge computer to make decisions in real-time within just a few milliseconds. 

Edge computing is different from cloud computing, which relies on the cloud or central location to process all data. With edge computing, the data is processed and stored locally where it was gathered. 

Edge computing is significantly better than cloud computing in that it allows for real-time millisecond data processing. Edge computing solves the problems associated with limited bandwidth and latency issues in some applications where calculations must be performed extremely quickly. 

Simply stated, edge computing allows you to process information locally instead of processing it in the cloud. Edge computers often gather information from sensors and IoT devices for local processing and decision making. Some edge PCs will then send post-processed information to the cloud, thus reducing the amount of required internet bandwidth.  

What is Edge AI Computing? 

Now that you know what computing at the edge means let’s discuss what edge AI computing refers to. Edge AI computing means running AI algorithms locally on an edge AI computer that’s close to the source of data that the algorithm is processing. AI Edge computing is great because it allows you to process data within milliseconds as opposed to the cloud, which may take seconds, giving you real-time information and decision-making capabilities for machine learning intelligence. 

Currently, most of AI processing is done in the cloud at massive data centers; however, a significant portion of AI processing is now moving to the edge, with many new smart applications choosing to process their raw data locally and then sending the processed data to the cloud for additional processing and analysis for deeper machine learning.  

What is Inference Analysis, and How is it Running at the Edge? 

Inference can now be performed at the edge instead of sending the data to the cloud, waiting for it to be processed and then returned. When performed at the edge, inference can be performed locally in real-time, thus reducing the amount of internet bandwidth required to process the data. 

If you want to perform AI at the edge, check out Premio’s excellent variety of edge AI computers here. We have a ton of different configurations that can be spec’d with different CPUs, GPUs, and connectors that you can choose from, all of which are major hardware technologies that assist with inference analysis and data telemetry back and forth at the edge.  

What Are Examples of Edge AI Computing? 

The best and most common examples of edge AI computing are semi-autonomous vehicles and/or commercial fleet trucks, such as the Tesla Model S and the Tesla Semi Truck. These vehicles are equipped with AI edge computers that collect data from the various sensors around the vehicle, process the information, and make decisions that operate the vehicle. Such information processing must be performed extremely quickly in real-time, something that’s not possible with cloud computing.  

What are the Advantages & Benefits of Edge AI Computing and Why is it Important? 

Edge AI computing is important for a number of reasons; here are some of those reasons: 

1. Real-time Data Processing

The most significant advantage that edge AI offers is that it brings high-performance compute power to the edge where sensors and IoT devices are located. AI edge computing makes it possible to perform AI applications directly on field devices. The systems can process data and perform machine learning in the field by using deep learning (DL) algorithms for autonomous applications, such as semi-autonomous vehicles. Imagine if it took your autonomous vehicle a few seconds to process data in the cloud vs. a few milliseconds to process it at the edge, accidents would occur too often, placing lives at a significant risk.

2. Privacy

Since much of the data processing with edge AI is performed at the local level on an edge computer, less data has to be sent to the cloud, thus reducing the risk that the data is mishandled or misappropriated as it sits somewhere in the cloud.  

That said, this does not mean that data secured is protected from hackers and other cybersecurity threats. The Trusted Platform Group sets a standard for hardware security in its TPM 2.0 module, which ensures methods for secure data storage, as well as encrypting authentication and integrity auditing of data. Learn more about TPM 2.0.  

3. Reduction in Internet Bandwidth

Since edge AI performs much of the data processing locally, you will save a lot of money on internet bandwidth because less data is transmitted through the internet. If you’ve ever used Amazon AWS AI Services, you probably know how expensive it can get to perform AI computation in the cloud. The cloud can be reserved now as a repository for post-processed data reserved for more analysis.  

4. Less Power

Since you’re processing data on a local level, you will save on energy costs since you will not have to remain connected to the cloud, transferring data back and forth between the edge device and the cloud. Additionally, many edge computing devices are engineered with power consumption and overall efficiency in mind. Since many edge applications are deployed in remote and uncontrolled environments, it's imperative for edge computers to balance power and performance.  

5. More Responsiveness

Since edge AI computers process data locally, they are much more responsive than waiting for a device to collect data, send it to the cloud for processing, and waiting for it to be sent back. This millisecond processing time makes it possible for edge AI to take extremely quick actions and make decisions just as fast. This makes edge AI computers great for applications that require real-time feedback, such as autonomous vehicles, intelligent automation, and robotics.  

What Can Edge AI Be Used For? 

1. Industrial IoT

Edge AI can be used in industrial settings, such as manufacturing, to automate the assembly line, as well as use AI to visually inspect products for defects. Having AI inspect products instead of human beings performing manual inspections can save you and your business a significant amount of money. Also, edge AI can inspect items and process information at much faster rates than human beings will ever be able to process. With Edge AI computer hardware coming down in cost, you could deploy it today at a massive scale.  
Learn More About Industrial Automation 

2. Surveillance & Monitoring

Edge AI can significantly improve surveillance and monitoring while reducing the amount of raw data that’s transmitted to the cloud. Before the introduction of edge AI, security cameras and sensors had to transfer enormous amounts of raw data to the cloud for processing and review. However, with the advent of edge AI, machine learning (ML) intelligent camera systems can capture raw data, process, and analyze it using facial recognition to identify persons of interest and suspicious activities that may be occurring directly at the edge.  

This significantly reduces the amount of raw data that must be transmitted to the cloud for processing. Only data that sets off certain triggers is sent to the cloud for further processing and analysis. This not only saves internet bandwidth but enables you to have more connected cameras and sensors without investing in extra infrastructure to support the ever-growing amount of connected devices and sensors. 
Learn More About Surveillance and Monitoring with Rugged NVR 

3. Autonomous Fleet Vehicles 

The importance of edge AI can be seen in its deployment in autonomous vehicles where real-time analysis is extremely important. Without real-time data processing, autonomous vehicles would not be possible. This is so because autonomous vehicles must make decisions with fractions of a second, something that would not be possible without edge computing. For example, a vehicle must identify road signs, pedestrians, lanes, other vehicles within milliseconds to operate the vehicle safely. If autonomous vehicles were to rely on the cloud for data processing that could take seconds to perform, collisions would increase because milliseconds matter when operating a vehicle. 
Learn More About In-Vehicle Telematics & Inference AI  

4. Better Management of IoT Devices

Edge AI can be used to better manage IoT devices. The term IoT refers to any device that can communicate with one another through the internet. This includes the cell phone in your pocket, electronic devices that you have in your home or business, and robotics. Edge AI can better store, manage, and process the data collected by these devices, placing less stress on the cloud for information processing. Using edge AI to manage IoT improves data processing and enables you to expand your network of IoT devices without having to invest in additional infrastructure to support the ever-growing number of IoT devices. 

The Future of Edge AI is a Bright One

It’s difficult to dispute the fact that most of AI computing is now headed to the edge. This is so because as the amount of data generated by IoT devices continues grow, there’s a growing need for the data to be processed at the edge. Also, concerns over privacy favor having data processed locally, which eliminates the need to send sensitive personal information for processing at the cloud. As such, the future of edge AI computing is a bright one indeed. Hopefully this blog post was able to answer what edge AI computing is, as well as how it's being used.