AB2 AML100 Dev Board Combines Analog ML with Renesas IoT Platform

Aspinity has created a machine learning core that utilizes completely analog circuitry to complete machine learning tasks. It goes by the name analogML and due to its analog nature, it offers users an ultra-low power solution for machine learning processing. The use of analog circuits to process sensor data allows the more power hungry circuits, such as digital signal processors and analog to digital converters, to remain asleep until relevant data is detected and a wake up signal is received. This can reduce a system's power consumption from milliwatts to microwatts. Which in applications such as edge ML can have a significant impact on the device's overall battery life.

The first chip offered from Aspinity that features the analogML core is the AML100. Natively processing analog data it will consume less than 20uA. It will support up to four analog sensors and is also field programmable. Furthermore, it is currently offered in a 7mm x 7mm 48-pin QFN package or users can pick up one of three development boards Aspinity offers. The most recent of these boards is the AB2 AML100 application board.

The AB2 AML100 application board is offered in the familiar Arduino form factor. As a result, it can support the many sensor interface and shield boards currently available on the market. Additionally, it features Renesas’ Quick-Connect IoT platform compatibility. This enables simple prototyping with the currently available hardware and software blocks from the Quick-Connect IoT ecosystem. For example, sensor and peripheral boards are available that can be daisy chained to one another, allowing extendable hardware. Furthermore, full access to the code and libraries for interfacing to hardware is available through Renesas most common integrated development environments and software workflows.

"System designers are continuously searching for faster ways to develop new and innovative IoT products. Our collaboration with Aspinity allows designers to add new features to their next generation power-constrained products without compromising the performance of MCUs. We are excited to continue to work with Aspinity on providing ultra-low power solutions for the myriad of always-on applications that require both high performance and an extended battery life,” explains Chris Allexandre, senior vice president at Renesas.

Various demonstrations have been provided by Aspinity showcasing the potential applications for the AML100. These include voice detection, vibration monitoring, acoustic event detection, and glass breaking. The glass breaking demo included the AB2 AML100 application board interfacing with Quick-Connect IoT platform’s EK-RA6M3 evaluation kit. The system was designed to detect glass breaking utilizing sensors. The entire system consumed less than 45uA of current when the AML100 is used for sensor listening and processing while the Renesas MCU is in deep sleep mode. Overall, the MCU stays in deep sleep mode for 99.9% of the time while the AML100 monitors sensors allowing for a significant battery life savings.