Abstract
Neuromorphic computing is a promising candidate for breaking the von Neumann bottleneck and developing high-efficient computing systems. Here we present a W/TaOx/Pt high-precision electronic synapse with excellent analog properties for neuromorphic computing. The device exhibits the potential of 10-bit weight precision, which is state of the art in conductance levels. Furthermore, the device shows linear weight update behavior in a specific conductance range, linear I-V curves in low voltage regime, long time retention, and precise modulation of weight. These characteristics are very helpful for improving the accuracy of neuromorphic networks. Finally, a $400\times 60\times 10$ three-layer perceptron was constructed with W/TaOx/Pt synapses for MNIST classification and ~92% accuracy was achieved.
Highlights
In the traditional von Neumann computing systems, the computing unit and memory unit are physically separated, leading to a lot of extra power consumption and latency time in data transmission process [1], [2]
Memristor, whose conductance can be changed according to the history of applied stimuli and maintained after removing stimuli, is an emerging nano-device and suitable for electronic synapse due to the simple structure, multi-level conductance, low power consumption and scalability [7]–[12]
We presented a W/TaOx/Pt memristor device with excellent analog property
Summary
In the traditional von Neumann computing systems, the computing unit and memory unit are physically separated, leading to a lot of extra power consumption and latency time in data transmission process [1], [2]. This issue is becoming a major challenge to develop high-efficient system for data intensive applications, such as Internet of Things (IoT) and Big Data [3], [4]. High performance electronic synapse is the fundamental building blocks for neuromorphic computing system. When integrated into a crossbar array, the memristor network has the ability to physically implement the
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