The astronomical growth in the use of Natural Language Processing (NLP) algorithms is rapidly accelerating the implementation of Edge and Fog computing and its principles.[1] NLP models such as Microsoft’s ChatGPT and Google’s Bard have sparked excitement about the future of these computing technologies, which are critical in the support of AI-powered devices.
As more organizations shift to processing data closer to where it is generated, the use of Edge and Fog will continue to surge, potentially even surpassing the adoption of cloud computing.
What are EDGE and FOG?
Edge computing is a popular computing paradigm that refers to a range of networks and devices at, or near, the actual user. Edge computing processes data closer to where it is generated, enabling processing at a more significant speed and volume. The result is more powerful action-led results in real-time.
Fog computing is a decentralized infrastructure that places storage and processing components at the edge of the cloud, where data sources such as application users and sensors exist.
Device Adoption
Regardless of the power or accuracy of the current AI tools, until their adoption extends to all devices, the vision for utilization will not be fully achieved. This necessitates that these powerful AI models directly deploy and run on smartphones and Internet of Things (IoT) sensors.
However, to adequately support AI features, the hardware for these devices must be upgraded.
With this, huge incoming demand in the chip market is forming for Google’s Tensor Processing Units and Microsoft’s Project Brainwave. The Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning using Google’s own TensorFlow software.[2] Microsoft Brainwave uses reprogrammable FPGAs (field-programmable gate arrays, a type of integrated circuit) at scale in Azure to speed up many kinds of operations, like network processing and machine learning.[3]
And let us be ready. Whether supported through iPhone or Android, future AI-enabled handheld devices may cost three times more than what we pay now.
Revolutionary Times
The future is promising as we look forward to the most powerful AI/ML technology breakthrough in quite some time. Bill Gates, in fact, compares the impact of this current AI revolution to the original advent of the Graphical User Interface (GUI), one that he knew quite well. [4]
With the many possibilities that lie ahead for these emerging technologies and the rate of change required to make them happen, this is certainly an exciting time to be a technologist.
[1] P. PunithaIlayarani and M. Maria Dominic, “Anatomization of Fog Computing and Edge Computing,” 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2019, pp. 1-6, doi: 10.1109/ICECCT.2019.8869125.
[2] N. P. Jouppi et al., “Ten Lessons From Three Generations Shaped Google’s TPUv4i : Industrial Product,” 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), Valencia, Spain, 2021, pp. 1-14, doi: 10.1109/ISCA52012.2021.00010.
[3] E. Chung et al., “Serving DNNs in Real Time at Datacenter Scale with Project Brainwave,” in IEEE Micro, vol. 38, no. 2, pp. 8-20, Mar./Apr. 2018, doi: 10.1109/MM.2018.022071131.
[4] Chow, Andrew R. “Bill Gates Believes Generative AI Will Be ‘Revolutionary’.” Time, Time, 21 Mar. 2023, https://time.com/6264801/billgates-ai/.
