Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence. State-of-the-art LSTM models with significantly increased complexity and a large number of parameters, however, have a bottleneck in computing power resulting from limited memory capacity and data communication bandwidth. Here we demonstrate experimentally that LSTM can be implemented with a memristor crossbar, which has a small circuit footprint to store a large number of parameters and in-memory computing capability that circumvents the 'von Neumann bottleneck'. We illustrate the capability of our system by solving real-world problems in regression and classification, which shows that memristor LSTM is a promising low-power and low-latency hardware platform for edge inference.
A simple experimentally accessible realization of current rectification by molecules (molecular films) bridging metal electrodes is described. It is based on the spatial asymmetry of the molecule and requires only one resonant conducting molecular level (π orbital). The rectification, which is due to asymmetric coupling of the level to the electrodes by tunnel barriers, is largely independent of the work function difference between the two electrodes. Results of extensive numerical studies of the family of suggested molecular rectifiers HS-(CH2)m-C6H4-(CH2)n-SH are presented. The highest rectification ratio ∼ 500 is achieved at m = 2 and n = 10.
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