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.

Neural Network Accelerators (NNA) may have become a must-have block in high-end mobile processors. Huawei Kirin 970, Samsung Exynos 9810, and Qualcomm Snapdragon 845 (via its Hexagon DSP) processors all come with NNA or NPU (Neural Processing Unit) to speed up tasks and comsume less power for tasks related to artificial intelligence / machine learning. Mediatek has also jumped on the bandwagon with Helio P60 chipset featuring four Cortex A73 cores, four Cortex A53 cores, an Arm Mali-G72MP3 GPU and a multi-core mobile APU (AI Processing Unit) leveraging the company's NeuroPilot AI technology. Helio P60 is said to deliver 70% higher performance, 25% power savings in heavy duty games, and overall power savings of up to 12% compared to the previous generation MediaTek Helio P23 series chipset. The APU offers double the power efficiency of the GPU for AI processing.