Lifelong-DNN emulates the way biological brains see the world and continuously learns from it. L-DNN pushes AI beyond on-device inference and allows it to learn on the device itself, significantly reducing the amount of data needed and training time and, enables real-time learning on-device. Today Lifelong-DNN powers over 50M devices globally, ranging from cameras, to smart phones, drones, and industrial machines.
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Neurala designed Lifelong-DNN after years of R&D with NASA. Today the technology is protected by over 25 awarded and pending patents.
NASA asked Neurala for a new Artificial Intelligence algorithm able to power an autonomous robot navigating an uncharted environment. The AI needed to not only control the robot, emulating aspects of human perception and motor control using very little compute power, but it also needed to learn new information on-device, without relying on internet connectivity or external compute resources. Back then, DNNs needed lots of compute power and time to learn new information and even today, traditional DNNs rely on this paradigm. L-DNN is the answer to the quest for AI that can learn continuously, the way we do.
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The Brain Builder SDK enables learning and inference at the edge, running on almost any kind of computer, from miniature single board computers to multi-GPU servers. Its lightweight DNN framework makes it portable from one OS / CPU / GPU type to another. Without having to send data to the cloud, data privacy and protection are maintained.
The patented L-DNN technology emulates the fast learning humans and animals perform on every piece of ‘data’ they encounter. L-DNN accelerated learning speed means that new learning can occur as new data is available. L-DNN keeps learning. Traditional DNNs do not.
Neurala’s AI can be trained with only acceptable, or good product images, unlike traditional DNNs that need both good and bad examples. Working with good samples only, L-DNN produces an anomaly signal when an alteration occurs. This results in tremendous time savings since defective products appear infrequently and randomly, and it would take a long time to collect their image data.
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