AI microcontroller boasts ultra-low-power design

This microcontroller is optimised to enable neural networks to execute at ultra-low power and live at the edge of the Internet of Things (IoT). The device combines energy-efficient AI processing with an ultra-low-power design.

This hardware-based CNN accelerator enables battery-powered applications to execute AI inferences while expending only millijoules of energy.

The MAX78002 is an advanced system-on-chip featuring an Arm Cortex-M4 with FPU CPU for efficient system control with an ultra-low-power deep neural network accelerator. The CNN engine has a weight storage memory of 2 MB and can support 1-, 2-, 4-, and 8-bit weights (supporting networks of up to 16 million weights). The CNN weight memory is SRAM-based so that AI network updates can be made on the fly.

The CNN engine also has 1.3 MB of data memory. The CNN architecture is highly flexible, allowing networks to be trained in conventional toolsets like PyTorch and TensorFlow, then converted for execution on the MAX78002 using tools provided by Analog Devices.

The device is supported by the MAX78002 evaluation kit, also available from Mouser Electronics.

Applications for the MAX78002 Artificial Intelligence microcontroller include factory robot and drone navigation, industrial sensors and process control, inline quality assurance vision systems, smart security cameras and portable medical diagnostics equipment.