Maxim Integrated Products and Aizip Inc., an AI company focused on IoT applications, have announced that the MAX78000 neural-network microcontroller from Maxim detects people in an image using the Visual Wake Words (VWW) model from Aizip at just 0.7 millijoules (mJ) of energy per inference. This is 100 times lower than conventional software implementations, and the most economical and efficient IoT person-detection system 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 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.
A key advantage of the efficient AI model and low power microcontroller system-on-chip (SoC) is the reduction in inference energy to 0.7 mJ, allowing 13 million inferences from a single AA/LR6 battery. Further, extreme model compression enables accurate smart vision with a memory-constrained, low-cost AI accelerated microcontroller and budget-friendly image sensors.
“The combination of Maxim Integrated’s ultra-low power chip solutions and Aizip's compact AI models is an important development that will enable many novel and exciting applications in the IoT world,” said Professor Bruno Olshausen at UC Berkeley, a highly recognized expert in neural computation/neural network models who also serves as an advisor to Aizip.
“The MAX78000 architecture, toolchain, and example code and models made it easy to get started and hit our accuracy, latency and power targets on schedule,” said Yuan Lu, Co-Founder and President, Aizip.