What is edge AI?
Edge AI processes and implements machine learning algorithms natively on hardware. This form of local computing reduces network latency for data transfer and solves the security challenge of everything happening on the device itself.
The Process of Edge AI
The local processing of Edge AI does not mean that the training of the machine learning model should happen locally. Typically, training takes place on platforms with greater computing power to process larger datasets. Finally, this trained model can be deployed on the processor or hardware of the system. The system features AI acceleration and a deployment model for real-time data processing applications.
Edge AI technology has experienced tremendous growth as demand for GPUs, NPUs, TPUs, and AI accelerators increases. This need is evident as machine learning and artificial intelligence have become trending technologies in the current situation. Therefore, Edge AI has found its place in hardware due to the demands of current applications. The need for local advanced processing and computing power in hardware explains the importance of Edge AI.
Can cloud AI outlive edge AI?
Cloud AI supports hardware processing by providing computing power remotely in the cloud. Since the processing is done remotely, the system is more powerful in terms of performance and processing. Additionally, cloud computing increases options regarding architecture and design. Since advanced processing happens on the cloud, it reduces the complexity of system hardware power consumption. However, as discussed in the introduction, these benefits come at the cost of latency and security concerns.
The process of cloud AI
Cloud AI can outlive edge AI when computing demands are very intensive and large data processing is required. If the application can compromise on latency and security, then Cloud AI is a better choice than Edge AI. Cloud AI can also address power consumption. However, it cannot be considered a deciding factor in choosing Cloud AI over Edge AI.
Edge AI vs Cloud AI
The uncertainty of choosing between Edge AI and Cloud AI occurs primarily in machine learning or deep learning use cases. Since deep learning algorithms require intensive processing, the performance of the hardware becomes an important factor. Cloud AI can definitely give the system better performance, but most deep learning applications cannot compromise on data transfer latency and cybersecurity threats. Therefore, Edge AI has a longer lifespan than Cloud AI for AI applications.
As mentioned earlier, power consumption will always affect edge AI processors. This is understandable since heavy computations require higher power supplies. But current Edge AI processors feature AI accelerators that provide higher performance and lower power consumption. However, GPUs and TPUs still require higher power, but improvements in design and circuit architecture will overcome this problem.
Since the cloud alone is not a great choice for AI applications, a hybrid of edge and cloud AI can provide better performance. Part of the processing that might affect latency can be done on the cloud, and the rest on the hardware itself.
Example: Since the trained model needs to be updated based on real-time data, the updated training can be done in the cloud. But real-time data is processed on hardware by Edge AI to generate output.
Thus, the division of processing brings the best of both techniques. Therefore, it may be a better choice for AI applications. However, most applications require faster real-time update training, so Edge AI has a longer lifespan than Cloud AI technology. As such, Edge AI is surpassing Cloud AI in deep learning applications.
Reviewing Editor: Guo Ting