Edge computing is a distributed, open IT architecture that features decentralized processing power, enabling mobile computing and Internet of Things (IoT) technologies. In edge computing, data is processed by the device itself or by a local computer or server, rather than being transmitted to a data center.
Allowing large amounts of data to be processed near the source, edge computing helps reduce internet bandwidth usage. This efficient data processing both eliminates costs and ensures that applications can be used effectively in remote locations. Plus, the ability to process data without ever putting it into a public cloud adds a useful layer of security for sensitive data.
Driven by a need to overcome cloud overhead in latency and bandwidth and a demand for more local processing, edge computing is poised to enable billions of new IoT end-points and real-time local artificial intelligence/machine learning (AI/ML) for autonomous systems. Edge computing allows smart applications and devices to respond to data almost instantaneously, as it’s being created, eliminating lag time, which is critical for technologies like self-driving cars.
How Client Devices Will Become Smarter
Robert Cihra, Managing Director and Senior Analyst at Guggenheim Securities, LLC, Research Division, says self-driving cars, smartphones and other client devices will become smarter in order to handle more local processing. According to Cihra, this is how:
- Making machines smarter via real-time on-board AI/ML
- Making thin-client smartphones fatter, as they need more processing and storage for on-device ML and virtual/augmented reality (VR/AR)
- Pushing smartphone configurations/BOM costs and thereby ASPs even higher
- Enabling more frictionless user interfaces (UIs) headlined by Voice and Vision vs. Keyboard and Screen
- Enabling data input from devices that increasingly involve no human interaction at all (e.g., cameras, IoT sensors for location, vibration, temperature, etc.)
- Favoring vertically-integrated vendors (hardware and software) particularly early on (e.g., Apple; Tesla; Google now building hardware; GM’s acquisition of Cruise Automation)
The Self-Driving Car Race
One of the hottest topics in edge computing is self-driving cars, because a self-driving car can’t be programmed to drive, but must think and act for itself, and it certainly cannot rely on the cloud and risk lag time.
The ability to process streams of sensor data and complex neural net pipelines in real-time is crucial. An autonomous car will require 50-100X the processing power and >10X the Dynamic Random Access Memory (DRAM) and Not And (NAND) technology of an Advanced Driver Assistance Systems (ADAS) car today.
Interior of Tesla Model S 90D car.
Cihra thinks Tesla, a pioneer in the American development of electric vehicles, is ahead of the curve in making automobiles an edge computing device. The company has used its connected fleet of customer cars for shared ML and building an in-house model that adds complexity, risk and cost, but also ultimate leverage.
As the perfect edge computing device, the automobile must be fully integrated, in terms of hardware and software development. And that’s why Cihra sees Apple either making a car itself or getting out of the market all together. Right now, Apple is investing in autonomous driving but has not yet committed to a car.
And This is Only the Beginning
Edge computing presents an incredible incremental growth opportunity for IoT development and data processing. To learn more about edge computing, check out the IEEE Introduction to Edge Computing course program, designed to give your organization critical foundational knowledge.
Resources
Ray, Tiernan. (2 Mar 2018). Apple, Tesla to Lead ‘Edge’ Computing, Says Gugenheim. Barron’s.
What is Edge Computing? Hewlett Packard Enterprise.
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