The internet of things has evolved from just connecting and transferring data between devices like sensors, cameras, and thermostats to making these devices smarter with decision-making capabilities. Thanks to machine learning (ML) and artificial intelligence (AI) technologies, that help these connected edge devices perform faster, in smarter ways.
Artificial intelligence plays a significant role in helping users analyze myriad of data generated by sensors and act upon them in a manner that is beneficial to users in different ways, such as environmental monitoring, weather analysis, predicting equipment failure in industries, disease prediction, etc. Machine learning and neural networks as parts of the AI technology help detect anomalies and patterns of data generated by sensors and devices, which help in extracting better insights for intelligent decision-making. AI-enabled IoT edge devices also help companies to increase operational efficiency and reduce downtimes, giving a competitive edge to business performance.
Let us understand how AI empowers smart and powerful devices on the network edge.
Inferencing and Training are the two main tasks of AI based on neural networks (ANN). Deep-learning or neural network in AI learns features and patterns of data generated by sensors and systems with a computer-intensive model. This process is called Training. Once the system/model is trained and deployed, inferencing compares the incoming data from devices against the trained model to make intelligent decisions.
Because of the advancement in algorithm optimization and improved computing resources in hardware, inferencing can now take place on devices at the network edge, without the need for cloud.
Inferencing at the edge relieves network bandwidth constraints, provides faster response, and lowers the cost of bandwidth and storage compared to cloud-based solutions. An example of this could be, an object tracking solution which can respond more effectively when it analyzes a video feed from the camera locally, instead of sending the feed from the network edge to the cloud for processing and waiting for the results. Inferencing at the edge also provides greater security and privacy, since data stay confined to the IoT device instead of being transferred over the network.
However, enabling AI on edge devices requires hardware that is powerful, low power with optimal inferencing performance. Qualcomm Technologies and its customers are among the early adopters of this concept. This early adoption and deployment have been possible due to key features of Snapdragon platforms and the supporting SDKs and frameworks provided by Qualcomm.
The latest Qualcomm Snapdragon 845 Platform has been optimized to improve the processing speed, leveraging heterogeneous computing through new Qualcomm Hexagon 685 Vector DSP architecture. The GPU and CPU optimizations deliver up to three times faster processing of neural networks running on-device compared to the prior generation SoCs.
In addition to the hardware, the Snapdragon 845 platform also supports Snapdragon Neural Processing Engine (SNPE) SDK. The Snapdragon NPE helps developers with software tools to accelerate deep neural network workloads on edge devices powered by Snapdragon processors. Developers can opt for the optimal Snapdragon core of the desired user experience – Qualcomm® Kryo™ CPU, Qualcomm® Adreno™ GPU or Qualcomm® Hexagon™ DSP.
The combination of Qualcomm Snapdragon SoC and SNPE that leverages optimized heterogeneous computing for high-performance, enables power-efficient AI on IoT edge devices for various applications including robotics, smart cities, home or industrial automation.
eInfochip’s Eragon products, based on Qualcomm Snapdragon processors, leverage the above-mentioned hardware and software features to kick-start your next-gen smart IoT solution development. Learn how Eragon 845 Leverages Qualcomm Snapdragon 845 Mobile Platform.