Author: EIS Release Date: Oct 14, 2020
Maxim has brought out a neural network accelerated MCU which claims to move AI to the edge without performance compromises in battery-powered IoT devices.
The MAX7800 is claimed to executeAI inferences at less that 1/100th the energy of software solutions dramatically improves run-time for battery-powered AI applications, while enabling complex AI use cases.
These power improvements are claimed to come with no compromise in latency or cost: the MAX78000 claims to execute 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 architecture works but is very challenging for edge applications due to poor latency and energy performance.
As an alternative, MCUs 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 MCU cores, the MAX78000 overcomes these limitations, enabling machines to see and hear complex patterns with local AI processing that executes in real-time.
Applications such as machine vision, audio and facial recognition can be made more efficient since the MAX78000 is said to execute inferences at less than 1/100th energy required by a microcontroller.
At the heart of the MAX78000 is specialized hardware designed to minimize the energy consumption and latency of convolutional neural networks (CNN).
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 Arm Cortex-M4 core, or the RISC-V core.
AI development can be challenging, and Maxim Integrated provides tools for a more seamless evaluation and development experience.
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.