Syntiant develops end-to-end deep learning solutions for always-on edge AI, pairing ultra-low-power Neural Decision Processors with edge-optimised models, training pipelines and MEMS sensors.
Founded in 2017 and headquartered in Irvine, California, Syntiant builds end-to-end deep learning solutions for always-on edge AI. The company couples purpose-built silicon with edge-optimised data platforms and training pipelines, producing ultra-low-power deep neural network processors that bring inference to the device rather than the cloud. Its stated mission is to make Edge AI a reality - delivering intelligence where people live, work and play, with an emphasis on privacy, responsiveness and seamless user experience.
At the centre of the product line are the Neural Decision Processors (NDPs), a family of ultra-low-power, high-performance deep neural network processors that deliver roughly 100x the efficiency and 10-30x higher throughput of existing low-power MCUs, enabling real-time processing at near-zero latency. Alongside them sit hardware-agnostic deep learning models engineered for world-leading inference speed and minimised memory footprints, able to run in compute-constrained environments spanning the largest GPU to the smallest MCU. Syntiant also ships market-leading MEMS microphones and vibration sensors across the Mobile, Ear and IoT markets.
The scale of that deployment is substantial: more than 20 billion sensors have been shipped to date, and the company is a recognised leader in edge AI deployments as well as a market leader in MEMS microphones and vibration sensors. Its work spans mobile devices, earbuds and wearables, IoT and consumer electronics, and it is backed by strategic investors including Intel Capital, Microsoft's M12, Applied Ventures and the Amazon Alexa Fund.
Technical work at Syntiant spans edge AI, deep learning, semiconductor design, ultra-low-power neural network processors, MEMS sensors, edge-optimised data platforms, training pipelines and inference optimization - a full stack from silicon to model, and a rare opportunity for engineers who want to shape how intelligence reaches the physical world.
