The AI that learns like a
biological brain

What L-DNN enables that conventional AI cannot

The architectural properties of L-DNN unlock capabilities that are simply not available in backpropagation-based deep learning.

Real-time on-device learning

New classes and objects can be added to a deployed model in seconds, without any cloud connection or server-side retraining. The product gets smarter in the field.

No catastrophic forgetting

L-DNN's memory consolidation mechanism preserves previously learned knowledge while adding new information, mimicking how biological memory actually works.

Trains from a handful of images

Achieves 98%+ accuracy from as few as 14 labeled images per class. No massive dataset collection, no labeling infrastructure, no data science team required.

Runs on standard edge hardware

Inference and training both run on a wide variety of CPU, GPU, and low-power processors.

Brain Melding™ - federated learning

Multiple L-DNN deployments can consolidate their learned knowledge into a single model, which enables federated learning across a fleet of devices without any cloud data transfer.

"Nothing I Know" - unknown rejection

Unlike conventional DNNs that always force a best-guess prediction, L-DNN knows what it doesn't know, reducing inaccurate results and improving accuracy and performance.

Three model types. Deployable today.

L-DNN includes three vision AI model types, each with the ability to train and run fully at the edge with minimal training data. Many products combine more than one with each type reinforcing the others to deliver richer, more capable results.

Classification

Is this X or Y?

Assigns an image or region to a category from a trained set. L-DNN learns each class from a small number of representative images and can add new classes in the field without cloud retraining or a GPU.

Example applications Product identification · Material & surface type recognition · Pass/fail grading · Part type sorting · Label and marking verification · Object categorization by visual property

Anomaly detection

Does this look right?

Learns what “normal” looks like from a small reference set, then flags anything that deviates, without requiring labeled examples of every possible defect or failure mode.

Example applications Surface irregularity detection · Packaging integrity verification · Seal and closure inspection· Foreign object detection · Contamination identification · Any scenario where failure modes are unpredictable or hard to pre-label

Detection

What is it and where is it?

Locates and identifies specific objects within a frame, returning bounding box coordinates alongside classification. Trains and runs fully on-device with no GPU, providing both identification and precise spatial position.

Example applications Label and text localization · Barcode and marking position detection · Object counting and tracking · Presence/absence verification · Spatial positioning for robotics · Multi-object scene parsing