Scaling-efficient in-situ training of CMOL CrossNet classifiers.

Abstract

CMOL CrossNets, hybrid CMOS/nanoelectronic neuromorphic circuits, may open up exciting opportunities to build artificial intelligence similar to the brain. However, limited functionality of nanodevices used in CMOL circuits causes significant challenges to train CrossNets with the usual algorithms. In order to overcome these challenges, we developed an in-situ variety of the error backpropagation method for supervised training of CrossNet-based pattern classifiers. Although this algorithm successfully trained CrossNets to perform simple benchmark classification tasks in Proben1, we found that it did not scale up to larger problems such as the MNIST dataset. Therefore, we propose an alternative in-situ method, combining training with the hidden layer build-up. Simulated results suggest that our new in-situ approach is appropriate to train CrossNets to perform classification on practical problems.