AI moving to the Edge of the Cloud
AI is moving towards the edges across sectors. Today, the Internet of Things and People’ s Devices (IoT&PD) at the edge of the cloud are more appealing to ML and AI applications because of the increase in their processing power. The Apple-designed 64 bit Cortex A10 ARM architecture has more than 1 billion of transistors with multicore processor to process approximately ~3.5 billion instructions per second.
A mobile phone CPU running at speeds of more than 1 GHz can execute millions of calculations each second. When compared with the processing rate of instructions of computers used to guide spacecraft to the moon 45 years ago, the mobile CPU today is more than ten thousands of times faster.
This move of AI to the edges is also allowing to further explore other approaches in the edges, such as power-law like approaches or fractal geometry to address intermittent phenomena and reduce operational costs.
OperAI predicted this trend, a few years ago and published its findings in this book titled “Edge & Fog Analytics: The New Analytics Interface”. OperAI focuses on AI at the edges (IoTs). OperAI is currently working to streamline operational processes at the edge of the cloud.
The findings are base on the trend research process involving gathering pertinent information and assessing different technology trends. The book refers to plethora of reports and publications in its assessment including assessment of how edge has been used in testing new operational approaches at the edge of the cloud. The book is available in print and electronic format at:
Edge and fog analytics, at the edges, reduce the latency between data capture and decision-making by acting immediately on streaming data, which may be required in critical remote operations. Both cloud and edge analytics will be supplementing each other in handling large-scale workloads and delivering data insights.
References
[1] A. Ferdowsi et al. (2017) Deep learning for reliable mobile edge analytics in intelligent transportation systems, arXiv preprint arXiv:1712.04135.
[2] Deep Learning for Signal Authentication and Security in Massive Internet of Things Systems A. Ferdowsi and W. Saad,” in Proc. IEEE International Conference on Communications (ICC), Kansas City, MO, USA, May 2018.
[3] Developers Will Adopt Sophisticated AI Model Training Tools in 2018 by James Kobielus (January 3, 2018 -https://www.datanami.com/2018/01/03/developers-will-adopt-sophisticated-ai-model-training-tools-2018/)
[4] Tibi Puiu (Sep 10, 2017) Your smartphone is millions of times more powerful than all of NASA’s combined computing in 1969. ZME Science newsletter
[5] A modern smartphone or a vintage supercomputer: which is more powerful? Posted: 14 Jun 2014, 20:28 , posted by Nick T.
[6] Lenovo expands edge computing portfolio with AI-focused Nvidia GPUs
By Andy Patrizio, Network World | JAN 17, 2022 3:00 AM PST
[7] A. Bari (2018) Edge & Fog Analytics: The New Analytics Interface. OperAI
