Human level perception
on
embedded
hardware
LGN
Achieving human level perception on embedded hardware is a major challenge for autonomous systems.
LGN delivers edge AI technology that helps autonomous systems see like humans on embedded hardware.
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Process high resolution sensor data with less power and compute
More intelligently
Focus your down stream processing on the important information
With less
Achieve high resolution imaging with an array of low cost sensors.
Unlock higher levels of autonomy
For autonomous vehicles to drive fast and reach high levels of autonomy they need to process an increasing number of high resolution sensors. This leads to increased compute cost and power consumption.
LGN boosts the resolution of lower cost sensors and optimally compresses sensor output for downstream machine learning. This reduces chip cost and power consumption whilst allowing high level autonomy on lower cost commodity sensors.
Unlock higher levels of autonomy
For autonomous vehicles to drive fast and reach high levels of autonomy they need to process an increasing number of high resolution sensors. This leads to increased compute cost and power consumption.
LGN boosts the resolution of lower cost sensors and optimally compresses sensor output for downstream machine learning. This reduces chip cost and power consumption whilst allowing high level autonomy on lower cost commodity sensors.
Smart buildings
Improve smart building security systems using facial and credential recognition.
Autonomous retail
Boost in-store detection systems for automated shopping and loss prevention.
Satellites
Optimise compression and transfer of satellite imaging and communications.
Request more information
Please use the form below to request our whitepaper or arrange a technology demonstration.
Team
Contact
London, UK
Tel:
+44 (0)20 3488 2138
+44 (0)20 3488 2138
Email:
[email protected]
Cylon, 27 Hammersmith Grove, London W6 0NE
West Midlands, UK
+44 (0)1743 612 006
+44 (0)1743 612 006
Old Grammar School House
School Gardens,
Shrewsbury SY1 2AJ
University of Zagreb, Croatia
Faculty of Electrical Engineering and Computing
Unska 3, HR-10000 Zagreb, Croatia