Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses

被引:201
作者
Ambrogio, Stefano [1 ,2 ]
Ciocchini, Nicola [1 ,2 ]
Laudato, Mario [1 ,2 ]
Milo, Valerio [1 ,2 ]
Pirovano, Agostino [3 ]
Fantini, Paolo [3 ]
Ielmini, Daniele [1 ,2 ]
机构
[1] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy
[2] IU NET, Milan, Italy
[3] Micron Semicond Italia, Res & Dev Proc, Vimercate, Italy
关键词
neuromorphic circuits; spike timing dependent plasticity; phase change memory; neural network; memristor; pattern recognition; cognitive computing; SYNAPTIC DEVICE;
D O I
10.3389/fnins.2016.00056
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on phase change memory (PCM) technology. The synapse is capable of spike-timing dependent plasticity (STDP), where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.
引用
收藏
页数:12
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