Neuromorphic hardware

Neuromorphic hardware is hardware that imitates the neuronal network of the brain.
Nodes in neuromorphic hardware are called neurons, and their networking is referred to as a neural network.
Neuromorphic hardware in digital was designed by IBM (TrueNorth), and in analog by many research groups (Boahen, Hasler, our lab, etc.). The best system uses analog computation and digital communication.
Using conventional digital hardware to simulate neuromorphic hardware (SpiNNaker) is clearly inefficient, because we are using 32 bits hardware to simulate 1-bit hardware.
Neural networks require multiply and add operations. The product of an M*M matrix by a M vector require M² multiplication and additions, or 2*M² operations. They also compute by convolutions, or artificial receptive fields, which basically are identical to matrix-vector multiplications, only that you multiply multiple values (filters) for each output data-point.