cognimem logo, hardwired parallel architecture, scalable, low power, decision space mapping, CogniBlox, CBX, 4K neurons, data mining, analytics, cognitive memory, sensor input, 4 CM1K's, 4096 cognitive memories, CM1K, 1K Neuron Chip, CM1K speed, High-speed pattern recognition, parallel architecture, non-linear classifier, CogniMem Architecture, CPU, HPC architecture, DSP, storage, CogniMem, Cognitive Computing, Artificial intelligence,clustering,data mining,expandable neural network,global sensing,Machine learning,neural network chip,neuron chip,neuronal processor,neurons,parallel neural network,pattern matching,pattern recognition chip,Restricted Coulomb Energy,smart sensors,super computing,trainable neural network
  • View Cart
  • Contact Us

The architecture of the CogniMem™ chip makes it the most practical implementation of a Radial Basis Function classifier with autonomous adaptive learning capabilities.

The Radial Basis Function is a classifier capable of representing complex nonlinear decision spaces using hyperspheres with adaptable radii. It is widely used for face recognition and other image recognition applications, function approximation, time series prediction, novelty detection.

RBF Decision

The CogniMem Advantage: Upon receipt of an input vector, all the cognitive memories holding a previously learned vector calculate their distance to the input vector and evaluate immediately if it falls in their similarity domain. If so, the “firing” cells are ready to output their response in an orderly fashion giving the way to the cell which holds the smallest distance. If no cell fires and a teaching command is issued, the next available cell automatically learns the vector. Also, if a teaching command conflicts with the category that a firing cell, the latter automatically corrects itself by reducing its influence field.

This autonomous learning and recognition behavior pertains to the unique CogniMem parallel architecture and a patented Search and Sort process.