Drift-Enhanced Unsupervised Learning of Handwritten Digits in Spiking Neural Network With PCM Synapses

Phase change memory (PCM), one of the most mature emerging non-volatile memories, has gained considerable attention over the years for use as electronic synapses in biologically inspired neuromorphic systems. The resistance drift of PCM devices, nonetheless, has long been identified as one of the biggest challenges toward realizing many areas of applications. Although this drawback has been extensively studied for memory development and many methods were proposed to mitigate the drift effect, its impact, if any, on online learning has not been fully explored yet. In this letter, we investigate the impact of resistance drift and variations in resistance drift parameters during unsupervised online learning. We use the resistance drift characteristics measured from experiments and incorporate them into the spiking neural network (SNN) for MNIST handwritten digits classification. Our results show that resistance drift, considered as a non-ideality for PCM devices, can be exploited to boost accuracy for online learning of handwritten digits in the SNN.

[1]  Shimeng Yu,et al.  Neuro-Inspired Computing With Emerging Nonvolatile Memorys , 2018, Proceedings of the IEEE.

[2]  Zhiwei Li,et al.  Binary neural network with 16 Mb RRAM macro chip for classification and online training , 2016, 2016 IEEE International Electron Devices Meeting (IEDM).

[3]  D. Ielmini,et al.  Physical interpretation, modeling and impact on phase change memory (PCM) reliability of resistance drift due to chalcogenide structural relaxation , 2007, 2007 IEEE International Electron Devices Meeting.

[4]  M. Breitwisch.  Phase Change Memory , 2008, 2008 International Interconnect Technology Conference.

[5]  D. Querlioz,et al.  Visual Pattern Extraction Using Energy-Efficient “2-PCM Synapse” Neuromorphic Architecture , 2012, IEEE Transactions on Electron Devices.

[6]  Yiran Chen,et al.  Reduction and IR-drop compensations techniques for reliable neuromorphic computing systems , 2014, 2014 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[7]  P. Debacker,et al.  Design-technology co-optimization for OxRRAM-based synaptic processing unit , 2017, 2017 Symposium on VLSI Technology.

[8]  Wei Lu,et al.  The future of electronics based on memristive systems , 2018, Nature Electronics.

[9]  Wolfgang Maass,et al.  STDP enables spiking neurons to detect hidden causes of their inputs , 2009, NIPS.

[10]  Xiaochen Peng,et al.  NeuroSim: A Circuit-Level Macro Model for Benchmarking Neuro-Inspired Architectures in Online Learning , 2018, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[11]  Chung Lam,et al.  Experimental demonstration of array-level learning with phase change synaptic devices , 2013, 2013 IEEE International Electron Devices Meeting.

[12]  Byoungil Lee,et al.  Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. , 2012, Nano letters.

[13]  Tao Li,et al.  Helmet: A resistance drift resilient architecture for multi-level cell phase change memory system , 2011, 2011 IEEE/IFIP 41st International Conference on Dependable Systems & Networks (DSN).

[14]  G. W. Burr,et al.  Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element , 2015, 2014 IEEE International Electron Devices Meeting.

[15]  S. Braga,et al.  Dependence of resistance drift on the amorphous cap size in phase change memory arrays , 2009 .

[16]  Byoungil Lee,et al.  Resistance and Threshold Switching Voltage Drift Behavior in Phase-Change Memory and Their Temperature Dependence at Microsecond Time Scales Studied Using a Micro-Thermal Stage , 2011, IEEE Transactions on Electron Devices.

[17]  Daniele Ielmini,et al.  Physical origin of the resistance drift exponent in amorphous phase change materials , 2011 .

[18]  Evangelos Eleftheriou,et al.  Projected phase-change memory devices , 2015, Nature Communications.

[19]  A. Pirovano,et al.  Low-field amorphous state resistance and threshold voltage drift in chalcogenide materials , 2004, IEEE Transactions on Electron Devices.