Brain Board

Own a piece of the neuromorphic revolution

The Nengo Brain Board implements a learning, spiking neural network that you can directly program from within Nengo.

These inexpensive boards open neuromorphics up to anyone. Explore and exploit the advantages of neuromorphic computing in the classroom, the innovation lab, or your basement.

Use Nengo’s friendly graphical interface to run parts of your standard Nengo model on power-efficient and fast neuromorphic hardware. These self-contained boards outperform most laptops in terms of both speed and efficiency.

Start by running one of the many demos that come with the board out of the box: adaptive controllers, visual classifiers, voice recognizers, and many more.

Brain Board Features

Onboard learning
Onboard learning
Inexpensive and off-the-shelf
Inexpensive and off-the-shelf
Use with any Nengo model
Use with any Nengo model
Easy setup
Easy setup
Contact us to purchase

Documentation

Documentation is freely available and frequently updated. Take a look to see how easy it is to get up and running.

Community

Join a growing community of people building Nengo models together.

Purchase a board

We offer complete hardware and software packages, or we can provide software for your hardware.

Research

Large-scale Nengo Brain Boards

Our small Nengo Brain Boards are just the beginning, developed to be inexpensive and targeting DE-1 and Pynq FPGA boards. To make neuromorphics easy and fast to deploy at scale, we’re developing larger and more capable versions with researchers at the University of Waterloo, which target large off-the-shelf Intel and Xilinx FPGAs. This will provide a quick route to get the benefits of neuromorphic computing sooner rather than later. This project is still in the early stages, but has already generated promising results, showing significantly better performance for high bandwidth and compute intensive use cases. Given the large scale, we’re working with collaborators to develop special communication protocols to move spiking data efficiently on chip.