Maxim launches neural network accelerator chip

October 08, 2020 //By Rich Pell
Neural network accelerator chip moves AI to the edge
Maxim Integrated Products has announced a neural network accelerator chip that enables IoT artificial intelligence (AI) in battery-powered devices.

The MAX78000 low-power neural network accelerated microcontroller is designed to move AI to the edge without performance compromises in battery-powered internet of Things (IoT) devices. It executes AI inferences at less than 1/100th the energy of software solutions, dramatically improving run-time for battery-powered AI applications while enabling complex new AI use cases previously considered impossible.

These power improvements, says the company, come with no compromise in latency or cost: 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.

"We've cut the power cord for AI at the edge," says 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 tradeoff, and we're excited to see a new universe of applications that this innovative technology enables."

By integrating a dedicated neural network accelerator with a pair of microcontroller cores, says the company, the MAX78000 overcomes previous 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 specialized hardware designed to minimize the energy consumption and latency of convolutional neural networks (CNNs). This hardware 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, says the company. 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.

The key advantages of the


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