摘要
In biological neural systems,noise is ubiquitous but does not affect the correct decisions made in the complex cognitive tasks.Decision-making in biological neural system is typically achieved by accumulating input information over a period of time.Inspired by recent developments in neurosciences,we design a decision-making module based on spintronic devices,utilizing superparamagnetic tunnel junctions as artificial neurons.The feasibility of this decision-making module is verified through circuit simulations.Taking a multi-layer perceptron as an example,the module significantly improves the accuracy of the perceptron in the handwritten digit recognition task.Furthermore,the spintronic decision-making module offers advantages over the conventional pooling methods,such as adaptive decision time,high performance and the absence of analog-to-digital conversion.The decision-making module is flexible to be integrated into artificial neural networks and provides a general yet effective solution to enhance performance against device noise.
基金
supported by the National Natural Science Foundation of China(Grant No.12174028)。
