NeuroMem® Technology

Associative Memories

  • Content addressable
  • No address, no index
  • Exact and fuzzy match
  • Fastest RBF
  • Uncertainty management
  • Fastest KNN

+ Trainable ANN

  • On-chip Learning
  • Intrinsic deduplication
  • Convergence
  • Traceability
  • Portability

+ Parallel Architecture

  • Low clock frequency
  • Low power consumption
  • Deterministic latency
  • Match 1 in N in < 10 µsec
  • Fixed low pin count
  • Seamless and high scalability

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

The Flavors of NeuroMem ICs…

 

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

  • 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
  • Chip Scale Package

NeuroMem IP

  • NeuroMem IP for FPGA or 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