Synaptogen: A Cross-Domain Generative Device Model for Large-Scale Neuromorphic Circuit Design

IEEE Transactions on Electron Devices, vol. 71, pp. 5345–5353

Abstract

We present a fast generative modeling approach for resistive memories that reproduces the complex statistical properties of real-world devices. By training on extensive measurement data of an integrated 1T1R array (6000 cycles of 512 devices), an autoregressive stochastic process accurately accounts for the cross-correlations between device switching parameters, while nonlinear transformations ensure agreement with the joint cycle-to-cycle (C2C) and device-to-device (D2D) write distributions. In addition to a high-level programming version, the model is also implemented in Verilog-A to enable efficient simulation of analog circuits. This statistically comprehensive model can be used to simulate crossbar sizes with up to 1024$\times$1024 devices, and benchmarks show that it achieves read/write speeds several orders of magnitude higher than a variability-aware physics-based compact model and over 10$\times$faster than even a simplified and deterministic compact model.

Authors 8

1. Tyler Hennen Aachen Institut für Werkstoffe der Elektrotechnik 2 (IWE2)

   RWTH Aachen University

   Affiliation as printed

   Institut für Werkstoffe der Elektrotechnik 2 (IWE2), RWTH Aachen University, Aachen, Germany

2. Leon Brackmann Aachen Institut für Werkstoffe der Elektrotechnik 2 (IWE2)

   RWTH Aachen University

   Affiliation as printed

   Institut für Werkstoffe der Elektrotechnik 2 (IWE2), RWTH Aachen University, Aachen, Germany

3. Tobias Ziegler Aachen Institut für Werkstoffe der Elektrotechnik 2 (IWE2)

   RWTH Aachen University

   Affiliation as printed

   Institut für Werkstoffe der Elektrotechnik 2 (IWE2), RWTH Aachen University, Aachen, Germany

4. Sebastian Siegel

   Forschungszentrum Jülich

   Affiliation as printed

   Peter Grünberg Institute (PGI-7), Forschungszentrum Jülich, Jülich, Germany

5. Stephan Menzel

   Forschungszentrum Jülich

   Affiliation as printed

   Peter Grünberg Institute (PGI-7), Forschungszentrum Jülich, Jülich, Germany

6. Rainer M. Waser
   Affiliation as printed

   IWE2, Aachen, Germany

7. Dirk J. Wouters Aachen Institut für Werkstoffe der Elektrotechnik 2 (IWE2)

   RWTH Aachen University

   Affiliation as printed

   Institut für Werkstoffe der Elektrotechnik 2 (IWE2), RWTH Aachen University, Aachen, Germany

8. Daniel Bedau

   Western Digital (United States)

   Affiliation as printed

   Western Digital Corporation, San Jose, CA, USA