Neuromorphic.labbrain-inspired chips

In the world

Where brain-inspired silicon earns its place

One pattern connects every successful neuromorphic deployment: continuous sensing, a strict power budget, and a need to react before the next frame would have arrived.

Robotics

Reflex-speed control loops that fit inside a battery budget.

  • Closed-loop motor control reacting in microseconds rather than frames.
  • Tactile and proprioceptive sensing fused as event streams.
  • On-chip plasticity lets a limb re-calibrate after wear or damage.

Autonomous vehicles

Event cameras plus spiking chips see motion, not frames.

  • Microsecond obstacle detection in high-contrast or low-light scenes.
  • Dramatically lower power than a rack of GPUs in the boot.
  • Graceful degradation: sparse spiking keeps working when data is noisy.

Healthcare

Implantable and wearable intelligence that runs for years.

  • Real-time seizure prediction from EEG on a milliwatt budget.
  • Neural prosthetics that speak the brain's own spike language.
  • Continuous cardiac and gait monitoring without cloud offload.

Edge AI

Always-on perception without an always-on data link.

  • Keyword spotting and gesture recognition at sub-milliwatt levels.
  • Privacy by construction: raw audio and video never leave the device.
  • Wake-on-event pipelines that keep the main processor asleep.

IoT & sensing

Sensors that think for a decade on one coin cell.

  • Predictive maintenance from vibration signatures in factories.
  • Environmental and gas-sensor arrays with on-chip odour learning.
  • Smart-agriculture nodes running on harvested solar energy.

Decision guide

Is your workload a good fit?

A quick heuristic before reaching for neuromorphic hardware.

Strong fit

  • Input arrives as sparse events rather than dense frames.
  • The device runs on a battery, a coin cell or harvested energy.
  • A response is needed in microseconds to low milliseconds.
  • The system must keep adapting after deployment.
  • Raw data must never leave the device for privacy reasons.

Poor fit

  • Exact numerical results are required to the last bit.
  • The workload is dense matrix maths on batched data.
  • Training a large model from scratch in a data centre.
  • Mature, highly tuned classical software already exists.
  • The team has no capacity to learn a new programming model.