Neuromemristive Extreme Learning Machines for Pattern Classification

This paper presents a novel neuromemristive architecture for pattern classification based on extreme learning machines (ELMs). Specifically, we propose CMOS current-mode neuron circuits, memristor-based bipolar synapse circuits, and a stochastic, hardware-friendly training approach based on the least-mean-squares (LMS) learning algorithm. These components are integrated into a current-mode ELM architecture. We show that the current-mode design is especially efficient for implementing constant network weights between the ELM's input and hidden layers. The neuromemristive ELM was simulated in the Cadence AMS design environment. We used an experimental memristor model based on experimental data from an HfO_{x} device. The top-level design was validated by training a 10 hidden-node network to detect edges in binary patterns. Results indicate that the proposed architecture and learning approach are able to yield 100% classification accuracy.