Edge computing transforms IoT by enabling devices to act on their data rather than just report it, addressing challenges of latency, security, and bandwidth. Simple use cases will evolve until the development of next-gen AI chips, which will enhance edge computing capabilities. While cloud computing is not suitable for all scenarios, edge computing offers valuable solutions where immediate decision-making and data privacy are crucial.

1. Edge Computing
or
IoT Learns to Interact
Paul O’Hagan
paulohagan@hotmail.com

2. Executive Summary
• IoT today is all about reporting & alerting from edge devices to a cloud
• Edge computing promises the ability for the edge to act on what it senses,
turning IoT into actors, not just reporters
• Edge computing addresses the three key issues with cloud computing:
Latency, Security & Bandwidth
• Initial opportunities will be simple if-then-else scenarios until next gen
processors purpose built for AI functions are productized

3. Edge Computing – Simplified View
• Edge computing is pushing the decision back to the data source for
faster actioning
• The world has progressed from centralized computing, to
decentralized, to cloud computing
• Edge computing is the next wave, that is just beginning. Not all
systems are suited for a cloud computing model

4. Cloud Computing – A Venn of Challenges
• Latency
• The decision needs to be made
immediately for highest value
• Security
• Regulatory compliance concerns
regarding secure transfer of data
between on-premise systems & sensors
• Bandwidth
• The associated high cost of transferring
data to & from the cloud
Latency Privacy
Bandwidth
Edge
Computing
Sweet Spot

5. The future of Edge Computing will be
AI tuned chips embedded in devices

6. Edge Computing – Use Cases
• AR & Maintenance Operations – the IoT device communicates with the AR
device to highlight the problem and tell the maintenance operator how to fix it.
• Neuroimaging – the MRI machine has an embedded AI that can recognize
anomalies at scan time, enabling faster patient response
• Smart Scanners – a portable scanner has an AI engine embedded, enabling the
decision about how to classify content to happen at the edge
• Voice Assistants – The AI engine is embedded into the VA device, reducing the
transmission of private information and increasing the speed of responses

7. Processor Gold Rush
• The task of processing AI is very different from standard computing or
GPU processing
• Software companies now building their own AI chips:
• Microsoft is building HoloLens specific chip
• Google is building Tensor Processing chip
• Amazon working on Alexa specific chip
• Apple working on Neural Engine chip for Alexa
• …
• The AI chip on the edge won’t replace the AI chip in the cloud. They
will complement with specialized inputs, processing and outputs

8. Summary
• Cloud computing isn’t the answer to every processing question
• Where privacy, latency and bandwidth concerns converge, edge computing
should be where the business looks for options
• Edge computing doesn’t require specialized AI chips to provide
business value
• However next generation AI specialized chips will change the edge computing
landscape