Augmenting effect of Edge AI over Cloud Computing for Deep Learning effects

Evolution of technology

In the past few years, the technological rise of the globe has been exponentially high. Last decade saw an enormous growth, both in terms of technology and in its acceptability by the people. Businesses all over the world are trying to adopt the new framework of the market. For this, companies are switching to online mode of businesses. The companies are spending extensive amounts of resources for their application and website development. A website or an application is the outer face of the company’s values and vision. The user interface has to be completely smooth and untroubled. For this companies prefer to use cloud computing services. Cloud computing services is a collection of independent remote servers hosted over the internet for storing, managing and accessing the data thus eradicating the need of personal computer or a local server. It’s highly efficient and effective services have made it extremely useful and resourceful for the companies. As the times have progressed, the evolution of technology has also limited the usage of cloud computing services over the newly developed technologies.

Understanding Edge AI

Edge AI technology is one of the highly developed and advanced services that is deeply automated thus reducing the system bugs and providing a highly secure platform. A large numbers of MCUs and hardware development boards are adopting the services of Edge AI technology nowadays though still majority prefer cloud computing services for its swiftness and simpler process for application designing. Basically Edge AI is a technology that process data generated by the hardware with the use of machine algorithms at a local level. The technology provides eminently less network delays for data transfer and additionally a more secured platform.

The system of Edge AI

The adoption of Edge AI local processing does not restricts the ML model training to a local level. Usually to process the larger dataset, a greater computational capacity platform is needed. And lastly, deployment of trained model takes place on the hardware or processor of the system. With extensive growth of machine learning and artificial technology the edge AI technology has gained a strong momentum in the technological flow. Also with the increased demands for GPUs, NPU’s, TPU’S and AI accelerators, it has received a strong boost.

The flow of Cloud AI

Cloud AI is good for intensive and heavy data processing with low power consumption, but this can’t be the basis of deciding if Cloud AI outlives Edge AI.

Edge AI and Cloud AI

Cloud AI has always been the most efficient service providers requiring high computation or bulk data processing. Cloud AI is definitely a considerable option for great results but the thing with deep learning applications is that it can’t sustain with security threats in network and latency with data transfer. Hence Edge AI definitely has an upper hand in artificial intelligence applications over the Cloud AI. Also, it is usually evident that with more bulk computations, higher power consumption is required. But Edge AI technology has been extremely low power consumer despite extremely high performance.