Learn on the chip. Stimuli can derive from text, bio-signals, audio files, images and videos, etc.

Real Time

Recognize patterns in micro-seconds regardless of the number of models stored in the neurons.

Energy Efficient

100,000 recognitions per second at 27Mhz. Less than 0.5 milliWatts per 1000 neurons.

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, recognizing or learning one pattern in a constant number of nsec and this regardless of the number of neurons committed in the chip
  • Behave collectively as (1) a K-Nearest Neighbor or (2) a Radial Basis Function (more specifically a Restricted Coulomb Energy classifier). They can cope with ill-defined and fuzzy data, high variability of context and novelty detection.
  • Collective built-in model generator which means that learning is done on the chip!
  • Multiple NeuroMem chips can be daisy-chained to scale a network from thousands to millions of neurons with the same speed performance and simplicity of operation as a single chip.
  • Amazingly simple to deploy

The Flavors of NeuroMem…


Today, several flavors of NeuroMem chips are available for your designs, but regardless of the chip you select, the access to the neurons will remain identical and their knowledge will be portable accross platform. This is made possible because all NeuroMem chips use the same bus and register map. Read more>>.


CM1K chip

  • 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

NeuroMem IP

  • NeuroMem IP for FPGA
  • NeuroMem IP for SOC
  • Custom neuron capacity
  • Custom memory capacity per neuron

Intel Quark SE

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