How does Edge AI benefit deep learning?

Edge-based deep learning brings many benefits

Depending on the target application of a deep learning model, it may require low latency, enhanced security, or long-term cost-effectiveness. 

Hosting deep learning models on the Edge instead of the cloud can be the best solution in such cases. 

This is because Edge deep learning runs deep learning processes on the individual device or in an environment closer to the device than the cloud. This reduces latency, cuts down on high cloud processing costs, and improves security.

What is Deep learning at the Edge?

Essentially, deep learning is a machine learning technique that trains computers to think the way humans do. Deep learning is accomplished when computers can learn by example.

By taking advantage of multi-layered network architectures and huge sets of data to continuously learn, these models can reach new heights in terms of accuracy. There are times when they can even surpass what humans can achieve! 

Deep learning trains machines to deal with any problem that necessitates the need to think, making it an essential technology across many industries.

End devices such as IoT sensors generate large volumes of data that need to be analyzed in real-time using deep learning. In turn, this data is also used to train deep learning models.

This is where the Edge comes into play, as both tasks require substantial computation resources to run quickly.  

Edge solutions, where compute nodes are placed close to the endpoints of a network, are a reliable way to meet the low-latency, high-computation requirements of deep learning on Edge-enabled devices. Not to mention that the Edge provides additional benefits in terms of bandwidth efficiency, privacy, and scalability, which will be explored in more detail later.

Although cloud AI can work well for a system, most deep learning apps can only function optimally with security threats or latency when transferring data. 

This is why Edge AI is a better choice for deep learning applications than traditional cloud-based AI options.

Since the cloud falls short in several key areas, a fusion of both cloud AI and Edge AI can offer enhanced performance.

For example, Edge AI can run deep learning models that take action and make decisions. At the same time, cloud services can continually learn from these models’  performances in the field to enhance AI and provide deeper insights.

Combining the reliability and high-volume data-gathering capabilities of Edge computing with the cloud’s processing power and storage capacity allows companies to run their IoT devices and applications with speed and efficiency.

The cloud is not enough to power deep learning applications

Deep learning-based applications and intelligent services have improved numerous aspects of individuals’ lives due to making strides in computer vision and natural language processing (NLP), among other fields. 

However, due to inefficiency and latency problems, current cloud computing architectures are insufficient to provide AI for various use cases.

For example, in a broad range of application scenarios, such as Face Recognition, smart cities and factories, and medical imaging, there are, unfortunately, a limited number of intelligent services offered. 

This is due to the following four factors:

1. Cloud-based deep learning comes with high bandwidth costs

Training deep learning models in the cloud requires devices to transmit massive amounts of data over long distances. 

This consumes immense network bandwidth and comes with a high price tag.

2. Latency is a problem in cloud-based deep learning

There can be a delay in accessing cloud services due to limited bandwidth, overcrowding, or connection issues. 

This delay can often be too long for many time-critical applications.

3. Deep learning in the cloud faces reliability issues

Most cloud computing applications depend on backbone networks and wireless communications for connecting users to services. 

Intelligent services must be highly reliable for many industrial use cases, even when network connections are lost. Unfortunately, the cloud can’t function without a reliable connection like Edge services.

4. Cloud-based deep learning compromises privacy

Deep Learning is a technology that often involves large quantities of private information. 

When deep learning is done in the cloud, it becomes susceptible to the privacy and security issues inherent in cloud technologies. 

Privacy is critical to sectors such as smart manufacturing, smart homes, and smart cities. In some cases, the transmission of sensitive data may not even be possible due to privacy laws and regulations. 

As the Edge is closer to users than the cloud, Edge computing and Edge AI have the power to solve the problems listed above.

The advantages of moving deep learning to the Edge

Edge computing and Edge AI can revolutionize deep learning by moving it to the Edge.

For example, security issues that come with storing sensitive information in the cloud are no longer an issue when switching to Edge solutions. 

This is because using Edge computing and Edge AI, inference and processing can all be done on-device, where users’ sensitive information is much more secure. 

Edge solutions also reduce the strain on cloud networks, improving bandwidth and freeing the cloud up for other important tasks.  

Real-time data processing is another tremendous advantage of Edge AI for deep learning.

As mentioned, with cloud computing and cloud-based AI, there will always be a delay due to data transfer times and bandwidth issues. This is a big problem as real-time data processing is imperative for medical devices, autonomous vehicles, and similar technology. 

Putting it simply, Edge AI enables deep learning to run faster while simultaneously making it more secure and affordable. 

Below are some significant benefits of deep learning that utilize Edge AI.

1. Deep learning at the Edge lowers costs

There’s no denying that cloud computing isn’t exactly easy on the budget.

Because enterprises constantly look for ways to cut down on expenses, Edge deep learning is the optimal choice. 

The moment deep learning is run closer to individual devices or machines, cloud computing, and bandwidth expenses drop.

2. Deep learning at the Edge reduces bandwidth usage

Deep learning at the Edge can save enormous amounts of bandwidth.

This is because devices can operate deep learning on whatever data they gather, after which the only data sent to the cloud is whatever remaining data the Edge device lacks the power to process.

Edge-enabled devices can also send data relating to whatever feedback is needed for improvement. 

This allows improvement and training data to be sent without overburdening the cloud.

3. Deep learning at the Edge offers an improved user experience

By cutting down data transfer time, deep learning that takes advantage of Edge AI can give instant feedback in various situations. This improves user experience and is critical in many use cases, such as home security.

4. Deep learning at the Edge reduces privacy risks

With Edge AI, less data gets moved around and shared, which means there’s also less chance of putting sensitive information at risk.

Additionally, because deep learning at the Edge can process data in real-time, source data can be deleted once it’s no longer required. 

Deletion of source data not only minimizes the need for bandwidth and storage space but also bumps up privacy.

5. Edge AI provides easier access to deep learning

As bandwidth requirements and cloud costs go down, more and more individuals and organizations will be able to reap the benefits of deep learning.

Four successful applications that utilize deep learning at the Edge

Now that we’ve made a case for deep learning at the Edge let’s explore how combining deep learning and Edge AI plays out in 4 real-life use cases.

1. Edge deep learning for commercial devices

Commercial devices like Amazon’s DeepLens are known for utilizing an Edge-based approach. 

In these instances, image detection is performed locally to reduce latency, and scenes are only uploaded to the cloud for remote viewing if an interesting object is detected. 

This saves bandwidth and frees up the cloud.

2. Natural language processing at the Edge

An excellent example of natural language processing at the Edge can be seen in voice assistants, such as Amazon’s Alexa and Apple’s Siri. 

While voice assistants perform some of their processing in the cloud, they typically use on-device processing to detect wake words (e.g., “Hi Siri”). 

Only once the wake word is detected is a voice recording sent to the cloud for further interpretation.

In the case of Siri and Apple, wake word processing uses two on-device deep neural networks that work to classify speech into 1 of 20 classes.

3. Edge deep learning for network functions

Deep learning can be used for network functions such as intrusion detection and wireless scheduling. 

Such systems, by definition, live on the Edge of networks and must operate under stringent latency restrictions. 

For example, an intrusion detection system that actively responds to attacks by blocking malicious packets must perform detection exceptionally quickly to avoid creating a bottleneck. As a result, Edge deep learning is perfect for the task. 

In-network caching is another example of a network function that utilizes deep learning at the network’s Edge.

In a cloud computing situation, different end devices located in the same geographical area may need to request the same content many times from a remote cloud server. 

Caching such content at an Edge server can significantly reduce response times and improve network traffic. 

There are generally two approaches to applying deep learning in a caching system. The first is to use deep learning for content popularity prediction, and the second is to use deep reinforcement learning to decide on a caching policy.

4. Edge deep learning in 360^◦ virtual reality

In 360^◦ virtual reality (VR), deep learning predicts the user’s field of view. 

These predictions are then used to determine which spatial regions of the 360^◦ video to gather from the content provider.

This information must be computed in real-time to minimize stalling and maximize the user experience.

Edge computing, and the low latency that comes with it, are required to provide satisfactory VR performance.

Edge-based deep learning will only grow in popularity

As can be seen, moving deep learning to the Edge brings many benefits. It provides a way to make deep learning technology more accessible to a larger pool of innovators and individuals. 

For these reasons, hosting deep learning at the Edge, instead of in the cloud, is a viable solution that will only gain traction as Edge AI and Edge computing solutions continue to flourish and make their way further into the mainstream.