Exploiting Intrinsic Variability of Filamentary Resistive Memory for Extreme Learning Machine Architectures

In this paper, we show for the first time how unavoidable device variability of emerging nonvolatile resistive memory devices can be exploited to design efficient low-power, low-footprint extreme learning machine (ELM) architectures. In particular, we utilize the uncontrollable off-state resistance (Roff/HRS) spreads, of nanoscale filamentary-resistive memory devices, to realize random input weights and random hidden neuron biases; a characteristic requirement of ELM. We propose a novel RRAM-ELM architecture. To validate our approach, experimental data from different filamentary-resistive switching devices (CBRAM, OXRAM) are used for full-network simulations. Learning capability of our RRAM-ELM architecture is illustrated with the help of two real-world applications: 1) diabetes diagnosis test (classification) and 2) SinC curve fitting (regression).

[1]  Csaba Andras Moritz,et al.  Hybrid Graphene Nanoribbon-CMOS tunneling volatile memory fabric , 2011, 2011 IEEE/ACM International Symposium on Nanoscale Architectures.

[2]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[3]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[4]  Paolo Gastaldo,et al.  Efficient Digital Implementation of Extreme Learning Machines for Classification , 2012, IEEE Transactions on Circuits and Systems II: Express Briefs.

[5]  Hongming Zhou,et al.  Extreme Learning Machines [Trends & Controversies] , 2013 .

[6]  Qinyu. Zhu.  Extreme Learning Machine , 2013 .

[7]  Shimeng Yu,et al.  Metal–Oxide RRAM , 2012, Proceedings of the IEEE.

[8]  B. DeSalvo,et al.  Emerging memory technologies: Challenges and opportunities , 2012, Proceedings of Technical Program of 2012 VLSI Technology, System and Application.

[9]  Shimeng Yu,et al.  On the Switching Parameter Variation of Metal Oxide RRAM—Part II: Model Corroboration and Device Design Strategy , 2012, IEEE Transactions on Electron Devices.

[10]  E. Vianello,et al.  Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses , 2013, IEEE Transactions on Electron Devices.

[11]  C. Gamrat,et al.  An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse , 2009, 0907.2540.

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

[13]  Guang-Bin Huang,et al.  An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels , 2014, Cognitive Computation.

[14]  Israel Koren,et al.  3-D integration requirements for hybrid nanoscale-CMOS fabrics , 2011, 2011 11th IEEE International Conference on Nanotechnology.

[15]  Hongming Zhou,et al.  Silicon spiking neurons for hardware implementation of extreme learning machines , 2013, Neurocomputing.

[16]  Allen Y. Yang,et al.  Fast ℓ1-minimization algorithms and an application in robust face recognition: A review , 2010, 2010 IEEE International Conference on Image Processing.

[17]  Jacques-Olivier Klein,et al.  Spin-transfer torque magnetic memory as a stochastic memristive synapse , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).

[18]  Victor C. M. Leung,et al.  Extreme Learning Machines [Trends & Controversies] , 2013, IEEE Intelligent Systems.

[19]  L. Goux,et al.  Intrinsic switching variability in HfO2 RRAM: A theoretical and experimental study , 2013, IMW 2013.

[20]  Rui Zhang,et al.  Characteristics of hafnium oxide resistance random access memory with different setting compliance current , 2013 .

[21]  Dhireesha Kudithipudi,et al.  A current-mode CMOS/memristor hybrid implementation of an extreme learning machine , 2014, GLSVLSI '14.

[22]  Ryan Kastner,et al.  An FPGA Design Space Exploration Tool for Matrix Inversion Architectures , 2008, 2008 Symposium on Application Specific Processors.

[23]  An Chen,et al.  Variability of resistive switching memories and its impact on crossbar array performance , 2011, 2011 International Reliability Physics Symposium.

[24]  Nagarajan Raghavan,et al.  Performance and reliability trade-offs for high-κ RRAM , 2014, Microelectron. Reliab..

[25]  Yi Ma,et al.  Demonstration of Conductive Bridging Random Access Memory (CBRAM) in logic CMOS process , 2011 .