At EdgeMachines we develop cutting edge end-to-end IoT & edge computing systems for the modern world using open tools, protocols and standards. Our hardware and software platform allows us to deploy AI-powered sensing on low-power devices without the need for expensive, high-bandwidth infrastructure. Using our AI models and FPGA & ASIC Accelerators we can create advanced IoT systems that gather and process complex data at the edge on the device. From harsh industrial environments to remote national parks, find out how our AIoT Platform can help monitor the places and assets you manage.
Optional depth vision
Hear everything
Embedded AI Acceleration
Exceptionally Power Efficient
Lower is better
* up to 3.5W when using spatial AI
Beyond simply monitoring noise levels in decibels, sound sensing uses our embedded AI hardware to continually process & classify sounds on the device. Because the device does all the processing, no audio data is ever recorded or transmitted off the device.
Impulse Sounds
Conservation & Protection
Classify environmental sounds to detect traffic hooning cars chainsaws gun shots crashes barking dogs yelling sirens construction fighting wildlife
Real-time identification of hazards in the workplace and home settings can be performed on device. Stero Vision allows our sensors to understand depth within a scene.
Stero Vision
Human-Machine Safety
We are now entering the age of edge computing, with devices becoming more intelligent and users become more privacy conscious, edge computing allows us to deploy cloud-processing-free sensor solutions. Performing computation & inference on the device at the edge, means devices require less. Our sensors are design to operate over low bandwidth, low power communication networks (LP-WANs) and are faster to deploy.
Privacy first design
Open protocols
By leveraging our base embedded device platform along with our expertise in data science, computer vision, machine learning and edge computing we can provide unique, practical solutions to previously infeasible or uneconomic sensing and data challenges.