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From on-prem to cloud, now edge computing… What’s next?

From on-prem to cloud, now edge computing… What’s next?

A few decades ago, computer science in the hardware field was a complex effort until the significant advancements in silicon semiconductors and the underlying technology used to build computers. In 1965, Gordon Moore made an observation that came to be known as “Moore’s law,” stating that the number of transistors on a chip doubled every year. This exponential increase in capacity has continued for over four decades, albeit at a slower pace.

As the computing paradigm based on hardware architecture evolved from on-premises to cloud, edge computing has taken the forefront. Its ability to replace cloud computing for most applications, coupled with added benefits such as lower latency, lower bandwidth, and faster processing speeds, has allowed companies to adopt it rapidly.

According to STL Partners, the global edge computing market is forecast to grow at a CAGR of 34 percent from 2019 to 2025. The report highlights the importance of IoT growth in driving the market. North America is estimated to be the largest market, with significant contributions from the manufacturing sector.

Some of the next computing paradigms are–

  1. Fog computing is a layer between edge computing and cloud computing. The processed data from edge devices may be sent to a middle fog layer, which consists of fog nodes or gateways that perform additional processing and might combine data from several edge devices. They decide which data should be sent to the cloud for further analysis or storage purposes.


  1. Quantum computing is based on quantum theory, which explains the behavior of energy and matter at the quantum level. The fundamental building block of a quantum computer is the quantum bit or qubit, capable of existing in multiple states at once. This unique characteristic enables quantum computers to handle numerous scenarios simultaneously, granting them the potential to surpass classical computers for specific assignments, such as complex optimization problems.


  1. Neuromorphic computing involves designing a computer architecture inspired by the human brain. By mimicking the structure and function of the brain, these advanced machines can significantly enhance the efficiency and effectiveness of machine learning applications. This approach enables machines to process information in a manner that closely resembles biological brains, making them adept at autonomous adaptation.

Looking forward

As Niels Bohr said, “Prediction is very difficult, especially about the future.” The dynamic computing landscape is poised to undergo a significant transformation in the near future, with an increasing focus on biological and quantum-inspired computations. Such technologies have the potential to address the most intractable computational problems.

As computing evolves, it is anticipated to follow in the footsteps of other fields, where incremental progress is the norm rather than the exception.

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