Heterogeneous CMOS/Memristor Hardware Neural Networks for Real-Time Target Classification

The advent of nanoscale metal-insulator-metal (MIM) structures with memristive properties has given birth to a new generation of hardware neural networks based on CMOS/memristor integration (CMHNNs). The advantage of the CMHNN paradigm compared to a pure CMOS approach lies in the multi-faceted functionality of memristive devices: They can efficiently store neural network configurations (weights and activation function parameters) via non-volatile, quasi-analog resistance states. They also provide high-density interconnects between neurons when integrated into 2-D and 3-D crossbar architectures. In this work, we explore the combination of CMHNN classifiers with manifold learning to reduce the dimensionality of CMHNN inputs. This allows the size of the CMHNN to be reduced significantly (by approximate to 9 7 %). We tested the proposed system using the Caltech101 database and were able to achieve classification accuracies within approximate to 1.5 % of those produced by a traditional support vector machine.