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. L-DNN keeps learning. Traditional DNNs do not. Today our patented technology powers Neurala's products in visual quality inspection applications for a wide range of industrial settings.
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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 needed a new AI 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 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 was the answer.
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Brain Builder, part of the Neurala VIA product, is a cloud-based solution that enables rapid AI prototyping, allowing users to annotate and train at the same time. Because of L-DNN's accelerated learning speed, learning can occur as new image data becomes available, so precision gains are instantaneous. Users can quickly iterate instead of having to wait hours, days or weeks to understand results.
Neurala VIA Inspector allows for seamless integration on the factory floor: collecting images from virtually any GigE vision camera, and providing outputs over an industrial network directly to the PLC controlling the applicable machine to run the vision AI models.
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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