OpenMV CEO and co-founder Kwabena Agyeman walks through the new OpenMV AE3, a 1 inch by 1 inch edge-AI camera board designed to embed machine vision and neural networks into literally anything – from light switches to wearables. Built as a tiny, battery-friendly “Arduino of machine vision,” the AE3 combines MicroPython programmability with around 250 billion operations per second of NPU compute so developers can deploy real-time vision models without touching the cloud. https://openmv.io/products/openmv-ae3
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Under the hood, the AE3 is powered by Alif Semiconductor’s Ensemble E3 SoC: dual Arm Cortex-M55 cores (400 MHz and 160 MHz) paired with two Ethos-U55 microNPUs delivering roughly 250 GOPS of AI throughput in that 6×7 mm package. On the board you get a 1 MP color global-shutter camera (up to ~120 fps at VGA), Octal-SPI flash streaming models at about 200 MB/s, integrated Wi-Fi/Bluetooth, high-speed USB-C, and a Qwiic connector for the SparkFun ecosystem. An onboard time-of-flight sensor, IMU (accelerometer/gyroscope), microphone and RGB LED turn the AE3 into a complete sensing and interaction platform rather than just a bare camera module.
In the demo, Kwabena shows the AE3 running hand-landmark detection at around 30 fps directly on the microcontroller, enabling gesture-driven user interfaces, as well as eye and face-landmark tracking, face recognition, segmentation and monocular depth estimation. The firmware stack supports a spectrum of neural network architectures – YOLOv2/v5/v8/v11 and custom CNNs – compressed to fit the constrained memory while still maintaining interactive frame rates. Because all inference runs locally, a smart door lock can recognize your face, or a smart light switch can see your hand, without ever streaming biometric data to a server.
He also explains how this board grew out of a decade of OpenMV work: eight years as a side project and the last two years full-time, culminating in a Kickstarter campaign for the N6 and AE3 that passed roughly $150k and around 700 backers. Filmed at Embedded World North America 2025, the conversation hints at where OpenMV is heading next – from battery-powered “AI in a golf cap” to industrial and robotics deployments – as MicroPython support for the Alif Ensemble family and the broader OpenMV ecosystem make it easier for engineers and makers to treat tiny AI vision as just another building block.
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