Neural network accelerator chip enables AIoT in battery-powered devices

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Maxim Integrated has launched the MAX78000 low-power neural network accelerated microcontroller, a devie that moves artificial intelligence (AI) to the edge in battery-powered IoT devices.

The device is able to execute AI inferences at less that 1/100th the energy of software solutions, so dramatically improving the run-time for battery-powered AI applications, while enabling complex new AI use cases previously considered impossible.These improvements, however, come with no compromise in latency or cost according to Maxim. The MAX78000 executes inferences 100x faster than software solutions running on low power microcontrollers, at a fraction of the cost of FPGA or GPU solutions.

In the past, bringing AI inferences to the edge meant gathering data from sensors, cameras and microphones, sending that data to the cloud to execute an inference, then sending an answer back to the edge. This is a very challenging architecture for edge applications due to poor latency and energy performance. As an alternative, low-power microcontrollers can be used to implement simple neural networks; however, latency suffers and only simple tasks can be run at the edge.

By integrating a dedicated neural network accelerator with a pair of microcontroller cores, the MAX78000 overcomes these limitations, enabling machines to see and hear complex patterns with local, low-power AI processing that executes in real-time.

Applications such as machine vision, audio and facial recognition can be made more efficient since the MAX78000 can execute inferences at less than 1/100th energy required by a microcontroller.

At the heart of the MAX78000 is specialised hardware designed to reduce energy consumption and latency of convolutional neural networks (CNN). It runs with minimal intervention from any microcontroller core, making operation extremely streamlined. Energy and time are only used for the mathematical operations that implement a CNN. To get data from the external world into the CNN engine efficiently, customers can use one of the two integrated microcontroller cores: the ultra-low power Arm Cortex-M4 core, or the even lower power RISC-V core.

“We’ve cut the power cord for AI at the edge,” said Kris Ardis, executive director for the Micros, Security and Software Business Unit at Maxim Integrated. “Battery-powered IoT devices can now do much more than just simple keyword spotting. We’ve changed the game in the typical power, latency and cost trade-off, and we’re excited to see a new universe of applications that this innovative technology enables.”

Maxim Integrated is also providing a comprehensive set of tools. The MAX78000EVKIT# includes audio and camera inputs, and out-of-the-box running demos for large vocabulary keyword spotting and facial recognition. Complete documentation helps engineers train networks for the MAX78000 in the tools they are used to using: TensorFlow or PyTorch.