Lifelong-Deep Neural Network™
technology: the only AI that gets smarter after every use

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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L-DNN Continuous Learning AI Technology
Robotic Arm With Deep Neural Networks Brain

Designed for NASA and Mars, brought down to Earth for real-world applications.

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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Brain Builder™: the AI platform that never stops learning.

Brain Builder incorporates Lifelong-DNN technology that enables rapid AI prototyping and deployment. This end to end approach allows users to annotate and train at the same time, using just a few images, instead of thousands. It offers instant feedback on performance, quickly iterating instead of having to wait hours or weeks to understand the results.

Advantages of Lifelong-DNN vs. Traditional DNN

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Less Data Required
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Instantaneous Learning
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Directly on small compute edge
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Continues to learn

L-DNN trains 10,000 times faster than traditional DNNs

Faster training gives significant advantages:

  • the amount of time and processing power required to augment the intelligence of the AI are negligible
  • every image can be used to augment the precision of the AI model
  • the precision gains are instantaneous, rather than having to wait for hours, days or weeks
Abstract Representation Of Deep Neural Networks

AI learning at the Edge

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.

Man Pressing Edge Computing Technology Button

More accurate than traditional DNNs

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.

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Abstract Representation of L-DNN AI Technology

Using only good product samples

Neurala’s AI can be trained with only GOOD image examples, 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 therefore take hours to update the network.

AI Technology Detecting Aluminum Parts On Assembly Line

L-DNN: Rapid Prototyping and Real Time Learning

By merging L-DNN with proprietary implementations, Neurala has solved shortcomings with traditional deep learning architectures. This enables rapid prototyping in AI model development, as well as real-time learning in deployed solutions, saving both time and money.
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Award-winning technology powering over 50M devices

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Optimized for a wide variety of platforms

Brain Builder can be deployed on a variety of operating systems, CPUs, GPUs and NPUs, from miniature single-board computers to large-scale servers.

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