Maxim Integrated Products' MAX78000 neural-network microcontroller detects people in an image using Aizip’s Visual Wake Words (VWW) model at just 0.7 millijoules (mJ) per inference. This is 100 times lower than conventional software solutions, and is presented as the most economical and efficient IoT person-detection solution available. The low-power network provides longer operation for battery-powered IoT systems that require human-presence detection, including building energy management and smart security cameras.
The MAX78000 low-power, neural-network accelerated microcontroller executes AI inferences at less than 1/100th the energy of conventional software solutions to dramatically improve run-time for battery-powered edge AI applications. The mixed precision VWW network is part of the Aizip Intelligent Vision Deep Neural Network (AIV DNN) series for image and video applications and was developed with Aizip’s proprietary design automation tools to achieve greater than 85 percent human-presence accuracy.
Key Advantages
Extended Battery Life: Efficient AI model and low power microcontroller system-on-chip (SoC) reduce inference energy to 0.7 mJ, allowing 13 million inferences from a single AA/LR6 battery.
Cost-Effective Intelligence at the Edge: Extreme model compression enables accurate smart vision with a memory-constrained, low-cost AI accelerated microcontroller and budget-friendly image sensors.