All-memristive neuromorphic computing with level-tuned neurons

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

[1]  Manuel Le Gallo,et al.  Stochastic phase-change neurons. , 2016, Nature nanotechnology.

[2]  Evangelos Eleftheriou,et al.  Learning spatio-temporal patterns in the presence of input noise using phase-change memristors , 2016, 2016 IEEE International Symposium on Circuits and Systems (ISCAS).

[3]  Haralampos Pozidis,et al.  Multilevel-Cell Phase-Change Memory: A Viable Technology , 2016, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[4]  Matthew Cook,et al.  Unsupervised learning of digit recognition using spike-timing-dependent plasticity , 2015, Front. Comput. Neurosci..

[5]  Giacomo Indiveri,et al.  Memory and Information Processing in Neuromorphic Systems , 2015, Proceedings of the IEEE.

[6]  Haralampos Pozidis,et al.  Phase-change memory: Feasibility of reliable multilevel-cell storage and retention at elevated temperatures , 2015, 2015 IEEE International Reliability Physics Symposium.

[7]  Pritish Narayanan,et al.  Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element , 2014, IEEE Transactions on Electron Devices.

[8]  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.

[9]  Eric Pop,et al.  Phase change materials and phase change memory , 2014 .

[10]  Daniel Krebs,et al.  Crystal growth within a phase change memory cell , 2014, Nature Communications.

[11]  Wulfram Gerstner,et al.  Neuronal Dynamics: Preface , 2014 .

[12]  Tobi Delbruck,et al.  Real-time classification and sensor fusion with a spiking deep belief network , 2013, Front. Neurosci..

[13]  Theodore Antonakopoulos,et al.  A versatile platform for characterization of solid-state memory channels , 2013, 2013 18th International Conference on Digital Signal Processing (DSP).

[14]  Geoffrey W. Burr,et al.  Nanoscale electronic synapses using phase change devices , 2013, JETC.

[15]  Giacomo Indiveri,et al.  Integration of nanoscale memristor synapses in neuromorphic computing architectures , 2013, Nanotechnology.

[16]  T. Serrano-Gotarredona,et al.  STDP and STDP variations with memristors for spiking neuromorphic learning systems , 2013, Front. Neurosci..

[17]  M. Pickett,et al.  A scalable neuristor built with Mott memristors. , 2013, Nature materials.

[18]  Damien Querlioz,et al.  Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity , 2012, Neural Networks.

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

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

[21]  Yong Liu,et al.  A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons , 2011, 2011 IEEE Custom Integrated Circuits Conference (CICC).

[22]  T. Hasegawa,et al.  Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. , 2011, Nature materials.

[23]  Haralampos Pozidis,et al.  Programming algorithms for multilevel phase-change memory , 2011, 2011 IEEE International Symposium of Circuits and Systems (ISCAS).

[24]  Bernabé Linares-Barranco,et al.  On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex , 2011, Front. Neurosci..

[25]  Dennis L Barbour,et al.  Level-tuned neurons in primary auditory cortex adapt differently to loud versus soft sounds. , 2011, Cerebral cortex.

[26]  C. Hagleitner,et al.  Device, circuit and system-level analysis of noise in multi-bit phase-change memory , 2010, 2010 International Electron Devices Meeting.

[27]  K. Gopalakrishnan,et al.  Phase change memory technology , 2010, 1001.1164.

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

[29]  Shih-Chii Liu,et al.  Computation with Spikes in a Winner-Take-All Network , 2009, Neural Computation.

[30]  Quan Zou,et al.  Kinetic models of spike-timing dependent plasticity and their functional consequences in detecting correlations , 2007, Biological Cybernetics.

[31]  M. Breitwisch,et al.  Novel Lithography-Independent Pore Phase Change Memory , 2007, 2007 IEEE Symposium on VLSI Technology.

[32]  Giacomo Indiveri,et al.  An event-based VLSI network of integrate-and-fire neurons , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[33]  Haim Sompolinsky,et al.  Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity , 2003, The Journal of Neuroscience.

[34]  G. Bi,et al.  Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.

[35]  L. Abbott,et al.  Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.

[36]  Carver A. Mead,et al.  Neuromorphic electronic systems , 1990, Proc. IEEE.