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Kathleen Martin
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Image Credit: 2022 IoT Technology Trends MOOR INSIGHTS & STRATEGY
Every year, after the CES dust settles, I take a step back and rank the most disruptive IoT-related technology trends. This year’s list signals the approach of a significant inflection point in the IoT growth curve.
These three developments accelerate IoT’s ongoing transformation from a hodge-podge of custom solutions into a growth industry built on platforms that plug-and-play within a software continuum extending from cloud services to edge devices. Plug-and-play platforms defragmented PCs and smartphones in the 2000s. The same thing is happening with IoT platforms, but the process is slower and more complicated because of the extreme diversity of devices.
Let’s look at each trend from a business perspective to see how smarter devices, standard device platforms, and open networks remove growth barriers and make the IoT more investable across all market segments.
Edge intelligence
Typical IoT devices have surprisingly little processing power because they act as “peripherals” to services running in the cloud or on-premises. These services ingest and analyze sensor data, trigger appropriate actions, send commands back down to devices, and interface with other services. A new generation of smarter edge platforms capable of autonomously running complex application software is now disrupting the peripheral model, which has been the norm for over 20 years.
Autonomous on-device analysis decreases response times while improving reliability and privacy for applications such as anomaly detection, predictive maintenance, wake-word recognition, image classification, and gesture control. Smarter devices also simplify development, shorten TTM, and reduce operating costs by enabling mainstream software development methods that do not require specialized embedded programming techniques. This year, four disruptive technologies combine to accelerate the edge intelligence trend.
2022 "Intelligent Edge" Technologies. MOOR INSIGHTS & STRATEGY
Here’s how IoT solution developers leverage these technologies to build increasingly intelligent edge applications.
Larger processors enable on-device applications to analyze and act on sensor data locally instead of sending it to the cloud for processing. Bigger chips also reduce device development costs and TTM by supporting advanced programming methods and tools such as cloud-native development and containerized software. Development productivity also increases as we move away from low-level embedded techniques and embrace modern, cloud-native software engineering methods.
Neural processors and accelerators (NPUs) enable edge devices to run surprisingly large ML inference applications. Until recently, math-heavy AI algorithms were practical only on large processors. New ML-accelerated chips enable small IoT devices, including battery-powered ones, to run ML inference applications locally without depending on cloud services. Welcome to the brave new world with AI integrated into the things all around us.
ML development on small devices has always been challenging because mainstream cloud-native machine learning code, models, and tools are not optimized for small platforms. In 2019, the tinyML Foundation started a new way of thinking about on-device ML by sharing ideas and experiences for ML applications on low-power devices. ML developers found ways to target small (down to 100kB and below), low-power (milliwatt) devices using familiar languages, tools, libraries, and workflows. Leveraging these concepts, Edge Impulse and other startups provide cloud-based development environments and run-time libraries that directly support many small IoT platforms, with or without NPUs. Cloud-native ML development brings AI functionality to low-power, always-on devices at the edge of the network.
Continue reading: https://www.forbes.com/sites/moorinsights/2022/03/03/2022-iot-technology-trends-the-era-of-iot-plug-and-play-begins/?sh=7abfede011ac
Every year, after the CES dust settles, I take a step back and rank the most disruptive IoT-related technology trends. This year’s list signals the approach of a significant inflection point in the IoT growth curve.
These three developments accelerate IoT’s ongoing transformation from a hodge-podge of custom solutions into a growth industry built on platforms that plug-and-play within a software continuum extending from cloud services to edge devices. Plug-and-play platforms defragmented PCs and smartphones in the 2000s. The same thing is happening with IoT platforms, but the process is slower and more complicated because of the extreme diversity of devices.
Let’s look at each trend from a business perspective to see how smarter devices, standard device platforms, and open networks remove growth barriers and make the IoT more investable across all market segments.
Edge intelligence
Typical IoT devices have surprisingly little processing power because they act as “peripherals” to services running in the cloud or on-premises. These services ingest and analyze sensor data, trigger appropriate actions, send commands back down to devices, and interface with other services. A new generation of smarter edge platforms capable of autonomously running complex application software is now disrupting the peripheral model, which has been the norm for over 20 years.
Autonomous on-device analysis decreases response times while improving reliability and privacy for applications such as anomaly detection, predictive maintenance, wake-word recognition, image classification, and gesture control. Smarter devices also simplify development, shorten TTM, and reduce operating costs by enabling mainstream software development methods that do not require specialized embedded programming techniques. This year, four disruptive technologies combine to accelerate the edge intelligence trend.
2022 "Intelligent Edge" Technologies. MOOR INSIGHTS & STRATEGY
Here’s how IoT solution developers leverage these technologies to build increasingly intelligent edge applications.
Larger processors enable on-device applications to analyze and act on sensor data locally instead of sending it to the cloud for processing. Bigger chips also reduce device development costs and TTM by supporting advanced programming methods and tools such as cloud-native development and containerized software. Development productivity also increases as we move away from low-level embedded techniques and embrace modern, cloud-native software engineering methods.
Neural processors and accelerators (NPUs) enable edge devices to run surprisingly large ML inference applications. Until recently, math-heavy AI algorithms were practical only on large processors. New ML-accelerated chips enable small IoT devices, including battery-powered ones, to run ML inference applications locally without depending on cloud services. Welcome to the brave new world with AI integrated into the things all around us.
ML development on small devices has always been challenging because mainstream cloud-native machine learning code, models, and tools are not optimized for small platforms. In 2019, the tinyML Foundation started a new way of thinking about on-device ML by sharing ideas and experiences for ML applications on low-power devices. ML developers found ways to target small (down to 100kB and below), low-power (milliwatt) devices using familiar languages, tools, libraries, and workflows. Leveraging these concepts, Edge Impulse and other startups provide cloud-based development environments and run-time libraries that directly support many small IoT platforms, with or without NPUs. Cloud-native ML development brings AI functionality to low-power, always-on devices at the edge of the network.
Continue reading: https://www.forbes.com/sites/moorinsights/2022/03/03/2022-iot-technology-trends-the-era-of-iot-plug-and-play-begins/?sh=7abfede011ac
