Inference Dropouts in Binary Weighted Analog Memristive Crossbar

Miniaturization and energy efficiency are essential for building reliable edge AI computing devices using memristive crossbar accelerators. We propose that stochastic dropouts in inference stages are essential to improve the operational energy efficiency. Further, we show that the binarization of weights under stochastic processing improves the robustness in a crossbar based architecture. The inference dropouts can reduce the power consumption of memristive crossbars without compromising the performance accuracy for up to 10-15% of dropped neurons, saving significant energy in the crossbar. The architecture shows robustness to variability, hardware noise, aging, and drifting of memristor levels and memristor failures.