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NT Adaptive NET

Series of neuromorphic controllers with STM32 microcontrollers and DIN Fasteners

NT Adaptive NET controllers has 2304 or 4608 neurons and STM32N microcontroller. This serie is special designed to work stand-alone without supervising computer and all units can be mounted on a DIN rails in instrument panels. All controllers can be combined to increase neural network.

All NT Adaptive AI controllers recognise various signals and makes programed action. Each NT Adaptive NET Controller has 4 or 8 neuromorphic memory chips NM500, and each chip has 576 neurons.

Artificial intelligence neural controllers can recognize static images, video stream, sounds, various electrical signals, text, data.

Signal recognition process takes place at the hardware level and without big help of a central processor. Controllers recognize signals in microseconds with only milliwats of power.


  • 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;

and others.

Areas of implementation:

  • Industry;
  • Agriculture;
  • Medicine;
  • Military;
  • Security;
  • Statistics;
  • Education;

and others.


Video Examples

All Video on YouTube >>>

The opportunity of neuromorphic to identify the positions of circuit breakers, LEDs indicating real equipment is presented.

The program is developed on Python and combines various options for demonstrating the possibility of neuromorphic chips. Recognition of positions of automatic power supply units, light indication of control lamps, indications of dial gauges, values of seven-segment indicators, positions of biscuit switches.

The demo program written in Python is shown. This demo program uses a combination of the OpenCV open library and the CM1K chip (NM500) when working with the PCIe expansion board. OpenCV was used to search for faces in photographs, the training and recognition of the faces themselves was performed using neuromorphic chips.

Demonstration of the program developed on Python. Demonstration of the sequence of image selection, ROI selection, selection and tuning of parameters of a neuromorphic chip, such as MAXIF, categories, training and verification of learning outcomes. This program uses neuromorphic chips on a PCIe board.

NT Adaptive NET dimensions: 106x96x50 mm

NT Adaptive NET controllers consist of two parts - a base with connectors, an ARM microcontroller, memory and a replaceable neural chip recognition board.

Connection to controllers can be via Mini USB or Ethernet.


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