NeuroMem Real-Time Machine Learning

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Pass/Fail, grade quality, locate defects, etc.


Locate people, measure cell density, detect obstacles, etc


Airplane, cars, people, cells under a microscope, etc.


Semi conductor inspection, QC of printed materials, etc


Deploy NeuroMem neural networks to learn and recognize your images and videos.

Try CogniSight SDK

Deploy NeuroMem neural networks to learn and recognize patterns extracted from any data sources, including images.

Try CogniPat SDK
  • Identification
  • Classification
  • Novelty detection
  • Anomaly detection
  • Image contextual segmentation
  • Tracking with reinforced learning as the target changes
  • Stereoscopic distance evaluation
  • Edge detection
  • Noise removal
  • Image compression


Supervised training
  • From annotations made by domain experts on collection of reference images
  • From live interrupts triggered by an operator
Non supervision training
  • To re-inforce a knowledge getting less confident
  • To synchronize knowledge between stereoscopic engines
  • To build a dictionnary of viewlets describing reference images for compression or segmentation
  • To build a reactive image memory frame
Train multiple networks to make more robust decision
  • Experts can be trained to be redundant to minimize false positive
  • Experts can be trained to be complementary to waive uncertainties wisely
Multiplicity of outputs
  • List of identified objects (location, recognized categories, confidence levels)
  • Transform images (maps of categories and confidence)


NeuroMem AI For Edge Intelligence

  • High speed recognition of sensory inputs
  • Novelty and Anomaly detection
  • Incremental and reinforced learning
  • Traceability


NeuroMem AI For Data Analytics

  • Exact and fuzzy data classification
  • Uncertainty management
  • Deterministic latencies and low power
  • High scalability

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