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Comparison of Off-Chip Training Methods for Neuromemristive Systems

Neuromemristive systems offer an efficient platform for learning and modeling non-linear functions in real time. Specifically, they are effective tools for pattern classification. However, training these systems presents several challenges, especially when CMOS and memristor process variations are considered. In this paper, we propose two off-chip training methods for neuromemristive systems: weight programming and feature training. Detailed variation models are developed to study the effects of CMOS and memristor process variations on neuromemristive circuits, including neurons, synapses, and training circuits. We analyze the impact of those variations on the proposed off-chip training methods. Specifically, we train a neuromemristive system to classify handwritten digits. The results indicate that the feature training method is able to provide over 2× better classification accuracy per unit area than the weight programming method. However, the weight programming method is much faster, and may be more suitable when the network needs to be frequently re-trained.