Memristor-Based Neural Image Processor Said to be 1000x Faster and Consume 10,000x Less Power

EETimes: Memristor research seems to be very popular in universities. University of Michigan develops a memristor-based neural image processor that is said to be 1000 times faster and 10,000 lower power than what we have today. The processor is able to recognize traffic signs and other features, based on neuron-like mechanisms. Wei Lu leading the project says:

"Basically there are two approaches we are developing, one uses small local memistors to store the weights that are calculated using well known learning algorithms, with most of the computations performed in the neuron. The other approach is more dramatic because we use the memristor to do the learning directly in its synapses, which is a riskier approach because you need a large amount of memory and the algorithms are not well known."

The project won DARPA funding of $5.7M for four years.