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How will Edge Intelligence Benefit IoT?

October 21, 2021

With edge artificial intelligence (AI) internet of things (IoT) devices are becoming smarter. Edge devices now have machine learning capabilities. This empowers them to process complex data locally, administer solutions and make predictions.

For instance, if you install edge IoT devices in your production plant, you can track the operating conditions of the machinery. With this, you will perform predictive maintenance, avoid complete system failure, associated damages, and costs. Smart cameras equipped with edge AI chips have uncovered new use cases beyond capturing video.

Such cameras can identify human traffic, monitor divers’ behavior, and count foot traffic. They can also classify and segment objects.

In this article, we will look at the benefits that edge intelligence has brought to IoT devices. You’ll learn more about edge AI chips and edge AI cameras that are part of the driving force in the IoT market.

How does AI at the edge benefit IoT devices?

Reduced bandwidth requirement

By moving resources closer to the devices that demand them, edge intelligence reduces delay and bandwidth requirements. With edge intelligence, data processing happens locally. And, less data is sent over the internet, which saves internet bandwidth.

Edge AI allows you to conduct your operations in real-time, so you only use cloud-based services for post-processing or large data sets.

Reduced latency

Intelligence edge computing is an enabler for latency-critical applications. It shortens communication propagation and latency. Compute intensive systems such as meteorology programs perform under ultra-low latency environments. Intelligence edge computing servers have the computing power enough to handle such systems in real-time.

Flexibility

Edge intelligence is flexible enough to deploy centralized or distributed solutions. This brings economies of scale, especially to cloud edge providers.

Cloud service providers can provide many cloud services with low latency in two ways:

  • Configuring the functionality of the control plane according to network proximity.
  • Deploying user-level instances in a distributed manner in the customer edge.

Virtualization and analytics

Intelligence edge computing compliments efforts to provide real-time insight into business operations. With this computing, businesses can anticipate future demand, deliver service innovation, and define operational efficiencies. Edge computing supports security data audits while providing data analytics in real-time.

How chip-enabled intelligent edge benefits IoT

Internet of things has limitations, some of which artificial intelligence, deep learning, and machine learning came to solve. For example, intelligence edge enhances IoT deployments by ensuring data processing happens closer to IoT devices. This way, IoT benefits in terms of improved data transport, efficiency, and low latency.

IoT devices involve a lot of artificial intelligence computations. These computations require varying data center-based chips to execute. This is because of their highly processor-intensive nature and this is not the only issue. The cost, power capacity, and hardware size make it a challenge to execute such computations in a small computing or storage system.

There was a need for a solution to address the problems related to traditional data-centers-based chips. Probably chips that would run at the device itself without a cloud connection. This is the feature that intelligent edge brought to chip technology. Now, AI techniques are embedded in IoT gateways and endpoints.

Edge AI chip industry

The edge AI chips market is anticipated to expand at a compound annual growth rate (CAGR) of 2. 27 percent from 2021. This will see the industry reach 2.09 Billion US dollars by 2028. Customers prefer IoT devices. As a result, this is pushing big players in the tech industry to invest more in the development of high-speed processors.

Both long-established chips makers and startups are focusing on adding artificial intelligence capabilities to the edge. These startups include Mythic, Hailo, Graphcore, EdgeQ, Flex Logic, Deep Vision, BrainChip, and Blaize.

STMelectronics, NXP, NVIDIA, Maxim, Intel, ARM, AMD, and Qualcomm have been making microcontrollers and microprocessors in the edge AI for quite some time now.

Benefits of chip-enabled intelligent edge

Chip-enabled edge intelligence improves the value of IoT devices in many ways.

Listed below are some of the benefits:

  • Edge AI chips generate less heat and use less power. They can be integrated with handheld devices like smartphones and other non-consumer devices like robots.
  • Edge-based AI chips reduce or end the need to send bulk data to a cloud solution or data center. This means processor-intensive machine learning computations happen locally. This promotes data security, privacy, and high processing speed.
  • Edge intelligence in 5G networks allows safe transportation or storage of critical data in a central location.
  • Edge AI chips help companies collect data from connected devices and analyze the data at the device. Thus, avoiding the complexity and security challenges. And, costs associated with sending and analyzing data on the cloud.

Edge intelligence and smart cameras on the IoT

Smart cameras with AI, IP connectivity, and advanced data analytics drive IoT innovations and new use cases. Chips on AI-enabled smart cameras empower IoT devices designers to create new solutions. And, products based on the power of edge artificial intelligence.

Video conferencing devices, dashboard cameras, and surveillance cameras now have new use cases. Such as facial recognition, people counting, body detection, license plate recognition. As well as, object segmentation and classification.

Chips on these cameras ensure that workloads like image processing, security, machine learning, and computer vision are kept at the edge device. Manufacturers of IoT devices have found a solution to network latency and user privacy. This has made them focus resources on innovation.

Edge AI cameras

AI dashboard-mounted cameras use edge processing for the detection of objects and events in real-time. They have helped improve the security, visibility, and safety of drivers, passengers, and pedestrians. This has helped to reduce vehicle accidents.

Edge AI cameras are built to run image-based deep learning and machine learning models at the local device. The dual-facing edge AI dash camera is common in parking lot management and traffic monitoring. Other edge AI cameras are used in sports broadcasting, smart farming, garbage management, and crowd analysis and monitoring.

Dual-facing edge AI dash cameras help in driver behavior analysis, fleet management, and street condition analysis. They record drivers’ anomalous behaviors. Such as texting or talking on the phone, dozing off, drinking, and eating and send notifications and alerts of such recurring instances. All these are key to generating drivers’ scorecards and lowering the risks of accidents.

In fleet management, these cameras record footage of unexpected braking, collisions, sudden accelerations, and so on. Such footage is uploaded to cloud storage automatically for later viewing.

Waste management companies have traditionally maintained mounted cameras on garbage trucks. But they are now replacing these cameras with edge AI cameras.

They can identify items such as metal scrap, plastic, styrofoam, cardboard, and paper. These cameras can also identify inappropriate and hazardous waste. This has helped improve waste disposal productivity and classification.

Conclusion

Edge intelligence is transforming the internet of things industry in a big way. It has made real-time data analysis easier. Also it helped increase operational efficiency, and reduced data handling and storage-related costs.

The ability of IoT devices to process advanced data in real-time has helped uncover new use cases. These include edge AI cameras for driver behavior analysis, garbage management, and fleet management.

With consumers embracing IoT devices with improved latency and privacy, more players will likely join the Edge AI industry. Already the Edge AI chips and Edge AI camera industries have attracted both long-established tech companies and startups.

Happy learning!

Further reading


Peer Review Contributions by: Dawe Daniel