NeuroMem ICs

Non linear classifier

Identify, classify, detect  novelty, cope with non linearity, report cases of uncertainty.


Model Generator

Learning on the chip, intrinsic deduplication, tracability of the knowledge built by the neurons


High performance

Fixed latency regardless of the number of neurons, µseconds per pattern, <150 mWatts

Available NOW

Available on ASICs, SOCs, and FPGAs and can be evaluated on a variety of platforms

Digital neurons inspired by biology

  • All interconnected and working in parallel following a Winner-Takes-All collective behavior
  • Recognize and learn a pattern in a constant time regardless of the number of neurons committed in the chip
  • Behave collectively as  a KNN (K-Nearest Neighbor) or RBF classifier (Radial Basis Function).
  • Cope with ill-defined and fuzzy data, high variability of context and novelty detection.
  • Multiple NeuroMem chips can be daisy-chained to scale a network from 1000s to millions of neurons with the same speed and simplicity of operation as a single chip.
  • Amazingly simple to deploy


  • 1024 neurons
  • 256 bytes of memory per neuron
  • Cascadable
  • I2C (optional use)
  • Recognition stage (optional use)

NM500 chip

  • 576 neurons
  • 256 bytes of memory per neuron
  • Cascadable
  • Wafer scale package

Intel Quark SE

  • 128 neurons
  • 128 bytes of memory per neuron
  • Component of an SOC also combining an x86 MCU and a sensor subsystem