Why artificial intelligence at the edge matters

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Why artificial intelligence at the edge matters

AI at the edge computing solutions
AI at the edge computing solutions

A few years ago, the concept of having personal assistant in your home, automobile or even phone that recognized your voice and could play your favorite music, shop, monitor your health, control home automation and handle a range of life’s chores was the work of science fiction. Since Apple unveiled Siri in 2011, we’ve seen strong growth in AI connected devices with digital assistants that now include Alexa from Amazon, Cortana from Microsoft and Bixby from Samsung, among several others.

Our lives can quickly be disrupted when our connected devices lose their internet connection. The AI used for voice recognition runs in the cloud, so no cloud access means no assistance. To safeguard our connected devices, couldn’t the device handle the task locally, without an internet connection? It could indeed, with Artificial Intelligence (AI) at the edge.

While the application of this technology to digital assistants represents one possible use, AI at the edge will have profound effects on many industries, including transportation, defense, manufacturing and healthcare to name a few. How will AI at the edge change these industries, and why does it matter?

 

What is AI at the edge?

AI at the edge means that AI algorithms can be processed locally on a hardware device. The processing algorithms use data (signals or sensor data) that originates from the device. A device using AI at the edge does not need to be continuously connected to the cloud in order to function properly — it can process data and make decisions independently without an internet connection.

 

AI at the edge moves intelligence closer to where it is needed: in the device

In order to employ AI at the edge, a device must include a microprocessor(s), sensor(s) and AI algorithm(s) in addition to the components with which it was originally designed to perform its set task.

One example is a battery-operated device that can connect to the cloud but has an AI at the edge software application running on a microprocessor processing data in real time. The data being generated is analyzed on the device and also stored locally. When needed, the device can connect to the cloud and transmit its data via the internet for redundant storage and long-term analytics. If the device were to continuously stream data to the cloud, battery life would be negatively impacted. In this simple example, AI at the edge can help extend the battery life of the device.

 

Machine-learning basics: training and inference

The two main phases of any machine-learning based solution are Training and Inference.

Training is a phase where a very large amount of known data is given to a machine-learning algorithm, for it to “learn” what to do. With this data, the algorithm can output a "model", containing the results of its learning. This step is extremely demanding in terms of processing power.

Inference is a device using the learned model with new data to infer what it should recognize. In short, the device interprets what is being asked and then completes the requested task.

The “known data” in the training phase is called labeled data. This means that each piece of data (e.g., sound, image, etc.) has a tag that describes what it is. A speech-recognition AI is trained with thousands of hours of labeled voice data in order to extract the text from a spoken sentence. Natural language recognition can then be used to convert the text into commands that a computer can understand.

Once trained, a device requires a fraction of the processing power to perform the inference phase, mainly because inference uses a single set of input data, while training typically requires a huge number of samples. The production model used for inference is also “frozen” — it cannot learn anymore— and may have less-relevant features trimmed out, and it is carefully optimized for the target environment. The net result is that it can run directly on an embedded device. Decision-making capability lies within the device, thus allowing it to be autonomous. This is AI at the edge.

 

Hardware challenges of AI at the edge on devices

Like a lot of new concepts, the technology behind AI at the edge has been around for some time: machine-learning algorithms are common in computers and smartphones, where they work just fine. But what about embedded devices? Well, the tools and hardware are now coming together to form a solution that makes sense, mainly thanks to:

• Increases in processing power of devices, and the availability of modules providing hardware acceleration for AI graphic processing units (GPUs) and application-specific integrated circuits (ASICs).

• Constant improvement of AI algorithm models and their performance

• The quality of the tools and resources that make the journey easier for data scientists, AI specialists and developers

Now we can integrate AI capabilities not only in supercomputers, but in cars, smartphones, web pages, Wi-Fi routers, factory robots or any number of applications ranging from small to large, simple to complex.

 

What does it take to efficiently embed inference on a device?

AI at the edge is first a matter of choosing the right hardware. AI inference on edge devices can be implemented on several hardware pieces.

CPU: On smartphones and embedded devices, any recent Arm CPU (Cortex-A7 and higher) is more than capable of handling AI at the edge. It may not be the fastest or most efficient solution, but it is usually the easiest. TensorFlow Lite software is commonly used and provides key features from the large TensorFlow framework.

GPU: Out-of-the box support for GPUs will vary, but they typically provide a large data throughput, allowing superior inference frequency and lower latency. The GPU also removes a large workload from the CPU which improves overall operating speed and efficiency.

AI specialized hardware: ASICs and tensor processing units (TPUs). These hardware components provide the most efficient AI solutions, but can be pricey and more difficult to design.

Let's further analyze GPUs and AI specialized hardware.

One possibility is to leverage the processing power and paralleling capabilities of GPUs. An AI is like a virtual brain with hundreds of neurons: It looks complex but is made of a large number of simple elements like neurons. This is where GPUs come into focus. Simple independent operations are applied to every single point (pixel or vertex) on your screen. Most machine learning frameworks (e.g., TensorFlow, Caffe, AML, etc.) are designed to take advantage of the proper hardware when it is present. Almost any GPU can be leveraged for these tasks.

Another solution is to integrate specialized hardware. Machine learning can be accelerated through custom hardware, and the two contenders are AI-specific chips and AI ASICs. Google has unveiled its Edge TPU, Arm has unveiled its Machine Learning and Object Detection processors, with Intel, Microsoft and Amazon all working on their own solutions as well. Currently, the best option is to have a GPU supported by the AI toolset you are using.

While AI at the edge might be a hot topic at the moment, it is not a passing fad, and it delivers real value.

 

Benefits of employing AI at the edge

1. Offline availability/latency: If an application needs to be available no matter the conditions and connectivity, intelligence must be handled locally. Latency or the complete loss of connectivity can happen due to unstable cellular data in a remote location, a DDoS attack or any number of factors. This is a huge challenge for cloud-based solutions. But if the intelligence is maintained locally on a device, there is nothing to worry about.

2. Lower cloud-service costs: Cloud services are convenient because of their scalability and availability, but represent a considerable recurrent cost that usually increases as a solution is more widely adopted. These costs are incurred throughout the life of a product. But if you were to sell a standalone device running the AI, you could significantly reduce its recurring costs and infrastructure needs.

3. Limit connectivity costs: Bandwidth and cellular data are expensive, too. Processing the information locally can greatly save on costs, by sending only the result of the AI’s computation for use in long-term analytics in the cloud. For a video security solution, megabytes of video data would transform into a few bytes.

4. Compliance and confidential information: Why send critical information over the internet when it can be gathered and processed locally? This does not mean that less concern should be placed on the security of devices, but it is one less thing to worry about, bringing peace of mind to customers.

5. Response time is critical: Gathering and processing data locally achieves faster response times which improves safety, performance and the user experience. This is only true if the device can process the data fast enough.

6. Think green: Processing data locally definitely makes sense in designing an efficient AI device. A small to medium IoT device will transmit about 1MB of data daily, which roughly equates to 20g of CO2. Compounded annually, 10,000 devices are responsible for up to 80 tons of CO2 pollution. Processing data locally could shrink this to under 1 ton, which is much better for the planet. Video or image-based solutions could realize a much greater impact.

 

What are the limits of AI at the edge?

AI at the edge is newer than cloud AI integration and intrinsically has some limits. A few of these limitations and risks are listed here.

1. Edge-capable devices: The AI will need a place to reside. It can be a mobile device like a smartphone or one that is as large as an autonomous vehicle. Whatever it is, it must be capable of running an AI solution efficiently.

2. Performance is limited by the device: An edge device will always have less processing power than an army of virtual servers. For this reason, it is important to identify from the start the response time and the complexity of a solution in order to properly design it.

3. Connectivity is needed to update the model: While inference is fast, training is usually a very long process, even on powerful, dedicated hardware. This is why it is performed on dedicated machines and cannot be done on the device level. After some time, a model will need to be updated to recognize new sentences, support new scenarios, perform new capabilities, etc., and the most efficient way to deploy this update is with an over the air (OTA) mechanism.

 

Making the move to AI at the edge can bring new efficiencies and opportunities

A few examples of how AI at the edge is effecting change.

Industrial applications:

• Greatly reduced equipment failure and downtime, which costs an estimated $50 billion annually.

• AI energy management with an average 20% reduction in power consumption during manufacturing.

• Intelligent oversight that uses local AI to quickly identify and resolve production problems.

Transportation applications:

• 24/7/365 functionality, maximizing equipment ROI.

• Reduction in labor costs.

• An estimated 60% drop in harmful emissions.

AI at the edge is the next step for many AI-based services. Maximum availability, data security, reduced latency and cost savings are all key strengths of tomorrow’s AI systems. As a company or an individual, now is a good time to make the move to this technology. While some might argue that it has not yet reached maturity, technologies are quickly advancing and converging. Experimenting and implementing a proof of concept is possible with limited effort, granting a competitive advantage before it becomes globally adopted. Given the exponential growth of AI and IoT, AI at the edge growth is inevitable, making it an excellent opportunity for investment.

 

Learn how we can make AI at the edge work for you. Contact us today.

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This white paper examines the Internet of Things market with a lens on the complexity of the vendor ecosystem and how partnerships are enabling holistic solutions to come to market.
smart phone with IoT icon overlay
OCF and Thread Group Alliance Boosts Windows 10 and its Smart Home Initiative
March 29, 2017
Open Connectivity Foundation (OCF) and Thread Group, recently collaborated to fire up the automated ‘smart home’ functionalities on the Windows 10 operating system.
conceptual graphic of IoT icons around a blue globe
An Introduction to IoT Components
March 29, 2017
From locomotives and jet engines to baby monitors and home appliance controls, new applications are challenging the imaginations of designers — both in startup companies and within giant corporations.
energy harvesting concept with green batteries sprouting from the ground
Powering the Internet of Things via Energy Harvesting
March 27, 2017
The push is on to add Internet capability to everything—often called the Internet of Things (IoT)—and the challenge for design engineers is to figure out how to power each of these IoT nodes.
night time cityscape with fast-moving traffic
10 Companies and Municipalities that Are Harnessing the Internet of Things to Run Smarter Cities
March 25, 2017
Many of the devices we interact with today are enabled by computers and connected to the Internet. Much of the popular interest in the Internet of Things (IoT) focuses on consumer electronics, with everything from smart refrigerators to thermostats,
man holding out his hand with icon overlay
The Internet of Everything is Here…or Is It?
March 18, 2017
The next great age of technology is the Internet of Things (IoT), if, that is, you believe everything you read on the Internet.
America flag with WIFI icon
It’s Time For The Government And The Internet Of Things
March 15, 2017
The Internet of Things or IoT has become quite the well-known term in the past year or two. What started out as a radical concept – connecting everything to everything else and to the Internet!
nurse checking person wearing health monitoring system on wrist
Internet of Things: Designing Sensor-Based Devices with Coin Cell Batteries
March 11, 2017
A popular vision of the Internet of Things (IoT) is that it will comprise billions of sensors gathering information about their local environment and transmitting that data back to servers in the cloud. Such data will be compiled, analyzed and shared
hand holding smart phone with IoT icons emanating from the phone
Indispensable Building Blocks of Mobile IoT Devices
March 10, 2017
The concept of IoT connected devices connotes many different ideas. Some think of data centers full of servers, network switches and storage arrays aggregating untold gigabytes of random data. Others identify with Ethernet-enabled devices in their pl
woman looking at tablet on airplane
The Internet of Things at Cruising Altitude: Airlines and the IoT
March 10, 2017
What are your thoughts when you step onto an airplane? “Where’s my seat?” “Is this a Wi-Fi flight?” “I hope I make my connection.”
shiping yard vehicles, airplan with IoT icons overlay
How the IoT will Change the Supply Chain?
March 10, 2017
The potential applications of Internet of Things (IoT) technology across any industry or sector are vast. Existing and future IoT applications promise new ways of value creation and revenue streams for businesses in a digital world.
bee polinating a puple flower field
What’s All The Buzz About? Bees And Smart Map Technology
March 8, 2017
You’ve heard about the problems with the bees, right? Their population numbers are dwindling and that’s not good news.
microprocessor chip being examined by worker
Zynq SoMs Decrease Customer Development Times and Costs
March 8, 2017
Xilinx’s Zynq®-7000 All Programmable (AP) System-on-Chip (SoC) provides a new paradigm for embedded system design.
two racks of hanging clothing
The Internet of Slacks? IoT and Smart Clothing
March 8, 2017
When you think of wearable technology you probably think about those fitness trackers that you wear on your wrist. What you probably don’t think about is your shirt, your pants or even your socks.
diagram of smartphone and connected devices
IoT Security: Real Problems and Solutions
By Guillaume Crinon   -   March 8, 2017
Learn about the real issues behind “IoT security,” and challenges for customers both from hardware and embedded / server software aspects.
key against a map of the world
Differentiated FPGAs Provide Security in Today’s Hyperconnected World
March 8, 2017
FPGAs offer the fastest way to integrate a specific design into a single device. See why an FPGA might be the perfect solution for your next project.
Yellow rainboots and gardening tools on flowerbed
The Internet of Things Has Thumbs – And They’re Green
March 8, 2017
It’s no surprise that technology, data and intelligent systems are transforming industrial agriculture.
puzzle piece put in place
An Overview of FPGAs: The Solution to Countless Design Challenges
March 7, 2017
The field-programmable gate array as a primary processing element offers many design, debug, and production benefits with few, if any, downsides.
man pointing to the word insurance
Risky Business: IoT’s Impact on Insurance
March 7, 2017
Risky Business: IoT’s Impact on Insurance By Steve Gereb August 25, 2016. I enjoy reading about how the data collected by Intelligent Systems will help people live better lives.
conceptual image of hand with legs and city background
Smart Cities: What To Look For In 2016 And Beyond
March 7, 2017
In a recent article in Government Technology, IDC research director Ruthbea Yesner Clarke discusses three trends emerging around the growing smart cities movement
woman on laptop with smart phone and smart watch
Will HaLow Open the Door for More Intelligent Systems?
March 7, 2017
Reporting on the recent announcement of the “long-awaited Wi-Fi HaLow standard for products incorporating IEEE 802.11ah wireless networking technology,” Jim Hunter of Tech Crunch explains why this new standard may pave the way for more connected
man holding tablet with chart image
Study: Digital Intelligence Driving Massive Business Transformation
March 7, 2017
The Internet of Things (IoT) is widely regarded as the next revolution in technology. Cisco predicts that by 2020, there will be 50 billion things connected to the Internet, generating revenues of $19 trillion.
bnr-one-click-manufacturing-could-It-really-be-that-easy
One-Click Manufacturing: Could It Really Be That Easy?
March 6, 2017
One click manufacturing (OCM) is a concept that arose from the 3D printing world: design a 3D model of a part ...
blue digital screen
LEDs: The Eyes and Ears of the Internet of Things
March 6, 2017
It may seem to be a stretch to assert that LEDs play a truly important role in the future of the Internet of Things. After all, by themselves, they’re just lights. However, with sensors tucked neatly inside and a bit of intelligence...
business man standing on small rowboat in the ocean
What To Do With All That Data
March 6, 2017
Just as important as the hardware and the data it collects in an Intelligent System are the tools we need to process and analyze information. Otherwise, an Intelligent System does nothing to serve our purposes.
blurry servers in a data center
Securing LED Networks in the Age of the Internet of Things
March 6, 2017
Recently, in Nuevo Arenal, a little sleepy village in the Guanacaste region of Costa Rica, a dozen criminals cut the lights to the town’s main street during the quiet 3 a.m. hour, and then injured...
graphic spelling out internet of things
Microsoft and GE Partnership to Make Industrial IoT More Accessible to Businesses
March 6, 2017
General Electric (GE) and Microsoft Corporation recently announced the beginning of a new alliance. The technology collaboration will make available GE’s Predix platform for Industrial Internet on the Microsoft Azure cloud for industrial businesses
beer taps in a row
Internet of Beer? Avnet Helps iKeg ‘Tap’ Into the Internet of Things
March 5, 2017
You might think that running out of beer is only a major concern if you are the “refreshment coordinator” for a college fraternity, but it turns out that beer inventory management is actually a pretty big deal for bar and restaurant owners these
cat and dog together on bed
Strange Bedfellows: Marketing and IT
March 5, 2017
The Internet of Things (IoT) is creating a special alliance between two business departments that aren’t always a natural fit: Marketing and IT.
military plane taking off
Defense/Aero Spinoffs Remain an Exceptional Source for Breakthrough Technology
March 5, 2017
Yes, automatic across the board cuts within the Department of Defense (DoD) will put the squeeze on many within the defense/aerospace supply chain.
hands with black gloves breaking into a laptop
Mitigating Cyber Attacks MCU Hardware Accelerators Boost
March 5, 2017
Over the years, cyber attacks on finacial institutions and news organizations have garnered the lion's share of attention...
conceptual image of IoT icons forming a cloud shape in a cloud server room
32-bit MCUs Penetrate IoT Ecosystem
March 3, 2017
While 8-bit and 16-bit MCUs are well suited for IoT end-node applications, the 32-bit MCU has a key function in the IoT pyramid: It provides the data throughput, reading sensors and sending real-time sensor data over an IP-based network.
hand navigating smart car dasboard
Automotive Electronics: Top 5 Tech Trends of Tomorrow’s Smart Cars
March 3, 2017
In the United States alone, motor vehicles travel well over four trillion miles each year according to the U.S. Department of Transportation.* To gain a sense of this distance, consider that while it takes light approximately eight minutes to travel
citi rooftops with conception IoT lights connected to one another
Antennas for RF Designs in the IoT
March 3, 2017
The physical connection for many IoT nodes leverages legacy wired networks found in homes, offices, schools, factories and other areas. As we become mobile, wireless physical connections are becoming the norm. Each wireless connection requires at lea
man touchign IoT wireless icons
Going Up? Elevators, Windows 10 And The Internet Of Things
March 2, 2017
People are excited about the Internet of Things for a variety of reasons. It’s already made us more connected. And as more and more things are connected, a number of industries are poised to become faster, more efficient and more reliable.
a fridge, diswasher, and smart phone on counter saying,
Alarm Bells Ring for Unsecured Connected Devices
March 2, 2017
A hot topic at the annual Black Hat cybersecurity conference in Las Vegas this summer was the increasing risk companies are facing with the Internet of Things (IoT).
network icons over an abstract color background
A “Canned” Module/IC Solution Simplifies Wireless Implementation, but Potential Design-in Issues Remain
March 2, 2017
Incorporating wireless connectivity into a product is now a standard design requirement for Wi-Fi, IoT, and other applications.
Pigeons perched on the side of a building
London’s Pigeon Population Flocks to IoT
February 25, 2017
In one of the strangest, most innovative uses of Intelligent Systems we’ve seen so far, DigitasLbi Creative Director Pierre Dequesnoy came up with the idea of using pigeons equipped with sensor-loaded backpacks to analyze pollution in the UK’s ca
leaves, colorful blocks with letter designations, CFL lightbulb in shape of flower
Internet of Things: Low Power, Low Cost Connected Devices Fuel Demand for Microco
February 25, 2017
At the heart of the devices that make up the Internet of Things (IoT) are a variety of ultra low power microcontrollers (MCUs), sensor networks, systems-on-chip (SoC) and communications protocols such as ZigBee. These underlying technologies operate
cyber security chip on circuit board
IoT Proliferation Teeters on the Edge
By Alex Iuorio   -   February 6, 2017
Cybersecurity and the irony of what happened during U.S. President Barack Obama's January 12, 2015 speech to the Federal Trade Commission on the growing threat, during which he declared: “If we’re’re going to be connected, we’ve got to be pro
illustration of clouds with data storage symbols
The Internet of Things Pushes the Possibilities Offered by Big Data
January 29, 2017
The concept of the Internet of Things (IoT) encompasses things as tangible as a light switch, things as ethereal as a cloud, and all things in-between.
3D cityscape with IoT icons
Invest in the Future with IoT
January 20, 2017
You and I have known for a long time that connected devices are becoming pervasive throughout society, and it seems like the investment world is finally catching up.
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