Edge AI is Overtaking Cloud Computing for Deep Learning Applications

By Saumitra Jagdale

Freelance Technology Writer

June 18, 2021


Edge AI is Overtaking Cloud Computing for Deep Learning Applications

You may have come across various hardware development boards and MCUs, which use Edge AI for processing and computations. However, cloud computing is still a preferred choice for deployment as it is faster and easier to design applications through the cloud platform. But it comes at the cost of latency in data transfer and security issues as the system is prone to network attacks. Hence, Edge AI addresses these issues as it works on the principle of on-device processing to make the system quick and safe.

What is Edge AI? 

Edge AI addresses the processing and the implementation of machine learning algorithms locally on the hardware. This form of local computing reduces the network delay for data transfer and solves the security challenges as everything happens on the device itself. 

The Flow of Edge AI 

Edge AI’s local processing does not mean that the training of the ML models should happen locally. Generally, the training takes place on a platform with a greater computational capacity to process a larger dataset. Finally, this trained model can be deployed on the processor or the hardware of the system. The system comes with the AI accelerating features along with the deployed model for real-time data processing applications. 

Edge AI technology has gone through tremendous growth with increased demand for GPUs, NPUs, TPUs, and AI accelerators. This demand is palpable as machine learning and artificial intelligence have become the trending technologies in the present scenario. Hence, Edge AI has found its place in hardware due to the requirement of the current applications. The need for local high-level processing and computational capacity in the hardware explains the significance of Edge AI.

Can Cloud AI Outlive Edge AI?

Cloud AI supports processing in hardware by providing computational power remotely on the cloud. As the processing takes place remotely, the system is more powerful in performance and processing. Also, cloud computing increases the options concerning architecture and design. It reduces the complication of power consumption of the system hardware as high-level processing occurs on the cloud. However, these benefits come at the cost of latency and security issues, as discussed in the introduction.

The Flow of Cloud AI

Cloud AI can outlive Edge AI when the computational requirement is very intensive and heavy data processing is needed. If the application can compromise with the latency and security, then Cloud AI is a better option than Edge AI. Cloud AI can also address the power consumption complication. However, it cannot be considered as the deciding factor for choosing Cloud AI over Edge AI.

Edge AI vs. Cloud AI

The uncertainty in choosing between Edge AI and Cloud AI mostly occurs for machine learning or deep learning use cases. As deep learning algorithms require intensive processing thus the performance of the hardware becomes a significant factor. Cloud AI can definitely provide better performance for the system, but most deep learning applications cannot compromise with latency in data transfer and the security threats in the network. Hence, Edge AI outlives Cloud AI for artificial intelligence applications. 

As discussed earlier, the power consumption factor always intervenes in Edge AI processors. It is understandable as heavy computations require a higher power supply. But the current Edge AI processors have AI accelerators that provide higher performance with low power consumption. However, GPUs and TPUs still require higher power, but the improvements in design and circuit architecture will overpower this issue.

Reference: https://lionbridge.ai/articles/what-is-edge-ai-computing/

As cloud alone is not an excellent option for AI applications, a hybrid of Edge and Cloud AI can provide better performance. Partial processing that can compromise with latency can be done on the cloud and the remaining part on the hardware itself. 

Example: As the trained model needs to be updated with respect to real-time data, this updated training can be done on the cloud. But the real-time data is processed on the hardware through Edge AI for generating output. 

Hence, the division of processing brings out the best of both technologies. Thus it could be a better option for AI applications. However, most of the applications need quicker real-time updated training, so Edge AI outlives the Cloud AI technology. Hence, Edge AI is overtaking Cloud AI for deep learning applications.

Saumitra Jagdale is a Backend Developer, Freelance Technical Author, Global AI Ambassador (SwissCognitive), Open-source Contributor in Python projects, Leader of Tensorflow Community India, and Passionate AI/ML Enthusiast.

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