By Reza Kazerounian and Camron Kazerounian
Reza Kazerounian
AI at the edge is too slow and power-hungry today: 32-bit microcontrollers need a major AI upgrade
Gigascale computing has enabled astonishingly fast advances in the capabilities of AI, dramatically changing the way that professionals perform functions as varied as scientific research, business report writing, and medical diagnosis.
These capabilities today depend on the computing power available in the cloud’s data centers, but embedded device manufacturers are eager for their products to take advantage of AI as well. In the embedded world, devices could use AI to respond instantly to the user’s voice commands, to predict when a machine may require servicing to avoid unplanned downtime, or to listen to and recognize the immediate sonic environment – with all AI operations performed locally, without reference to the cloud.
But if this vision is to become a reality, manufacturers of microcontrollers (MCUs) will need to re-engineer their products, tailoring them for the power, cost, and size constraints common in the embedded world.
AI: the tool for handling growing data volumes at the edge
The number of devices deployed at the edge is growing, and the amount of data that each produces is also growing fast. This is driving the need for on-device AI processing. As the global datasphere continually expands – it is forecast to quadruple to close to 600 zettabytes (ZB) by 2030 – the sheer volume of data threatens to overwhelm traditional cloud computing systems.
