Maxim Integrated Products and Aizip Inc have announced that Maxim’s MAX78000 neural-network microcontroller detects people in an image utilising Aizip’s Visual Wake Words (VWW) model at only 0.7mJ of energy per inference. This is 100 times lower than conventional software solutions and the most economical and efficient IoT person-detection solution currently available. The low-power network offers longer operation for battery-powered IoT systems that need human-presence detection, including building energy management and smart security cameras.
The MAX78000 low-power, the neural-network accelerated microcontroller executes AI inferences at lower than 1/100th the energy of conventional software solutions to significantly increase run-time for battery-powered edge AI applications. The mixed precision VWW network is part of the Aizip Intelligent Vision Deep Neural Network series for image and video applications and was developed with its proprietary design automation tools to deliver greater than 85% human-presence accuracy.
“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 recognised 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.
“Aizip was quick to exploit our per layer quantisation capability to reduce weight storage and achieve a compact, energy-efficient model for human detection. I look forward to working with them on future projects,” said Robert Muchsel, Maxim Integrated fellow and architect of the MAX78000 microcontroller.