Achieving ideal accuracies in analog neuromorphic computing using periodic carry

Analog resistive memories promise to reduce the energy of neural networks by orders of magnitude. However, the write variability and write nonlinearity of current devices prevent neural networks from training to high accuracy. We present a novel periodic carry method that uses a positional number system to overcome this while maintaining the benefit of parallel analog matrix operations. We demonstrate how noisy, nonlinear TaO
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devices that could only train to 80% accuracy on MNIST, can now reach 97% accuracy, only 1% away from an ideal numeric accuracy of 98%. On a file type dataset, the TaO
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devices achieve ideal numeric accuracy. In addition, low noise, linear Li
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CoO
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devices train to ideal numeric accuracies using periodic carry on both datasets.