Company: Maxim Integrated
Category: Embedded Solution Product of the Year
The MAX78000 low-power neural network accelerated microcontroller from Maxim Integrated moves artificial intelligence (AI) to the edge without performance compromises in battery-powered IoT devices. Executing AI inferences at less than 1/100th the energy of software solutions dramatically improves run-time for battery-powered AI applications, while enabling complex new AI use cases previously considered impossible. These power improvements 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.
AI technology allows machines to see and hear, making sense of the world in ways that were previously impractical. 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, 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.
As a semiconductor company, Maxim Integrated’s goal is to design and sell silicon that enable new features or capabilities for our customers. With this product, we have targeted a unique space: there is a large gap between what low power microcontrollers can do with AI (simple keyword spotting, simple sensor analysis) and what high end GPU and FPGA products can do (high frame rate, high density image analysis). We can bring more complex audio capabilities like multi keyword spotting to battery powered devices, or enable lower end vision (moderate frame rate, moderate image density) in battery powered devices. Our strategy is to market this technology to manufacturers of power and space constrained devices who haven’t been able to use AI technology since it hasn’t been available for this kind of equipment.