Exploring Edge AI: NPUs, market and tech trends, processor IP and microcontroller chips ranked and analyzed

Edge AI is all about having a brain like computing processor IP core in the processor/microcontroller chip in the edge/terminal devices such as IoT embedded boards and any such devices where the processing of video/audio, sensor, and any such data is processed locally on devices. More examples include smartphones, security cameras, autonomous vehicles, and industrial IoT systems. Edge AI refers to the deployment of AI models and algorithms on devices at the network's edge.
With the availability of reliable high-speed network, edge devices were leveraging that feature to transfer complex processing tasks to cloud, enabled them to handle highly complex data feeds.  With the ample availability of computing resources even in smaller version of processors, now the trend is towards more computing at edge with lesser and lesser dependency on cloud.  Local processing gives the advantage of speed, privacy, and efficiency. The ability to make real-time decisions without relying on the cloud is not just convenient, it’s essential for applications where every millisecond counts, like autonomous vehicles or medical diagnostics.

Key benefits of edge AI include:
Reduced Latency: Local processing eliminates delays from data transmission to the cloud.
Lower Bandwidth Usage: Less data is sent over networks, preserving internet resources.
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