System model of neuromorphic sequence learning on a memristive crossbar array

Neuromorphic Computing and Engineering, vol. 3, pp. 024002

Abstract

Abstract Machine learning models for sequence learning and processing often suffer from high energy consumption and require large amounts of training data. The brain presents more efficient solutions to how these types of tasks can be solved. While this has inspired the conception of novel brain-inspired algorithms, their realizations remain constrained to conventional von-Neumann machines. Therefore, the potential power efficiency of the algorithm cannot be exploited due to the inherent memory bottleneck of the computing architecture. Therefore, we present in this paper a dedicated hardware implementation of a biologically plausible version of the Temporal Memory component of the Hierarchical Temporal Memory concept. Our implementation is built on a memristive crossbar array and is the result of a hardware-algorithm co-design process. Rather than using the memristive devices solely for data storage, our approach leverages their specific switching dynamics to propose a formulation of the peripheral circuitry, resulting in a more efficient design. By combining a brain-like algorithm with emerging non-volatile memristive device technology we strive for maximum energy efficiency. We present simulation results on the training of complex high-order sequences and discuss how the system is able to predict in a context-dependent manner. Finally, we investigate the energy consumption during the training and conclude with a discussion of scaling prospects.

Authors 6

1. Sebastian Siegel corresponding

   Forschungszentrum Jülich

   Affiliation as printed

   Peter-Grünberg-Institute (PGI-10), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Strasse, Julich, 52428, GERMANY

2. Younes Bouhadjar

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Strasse, Julich, 52428, GERMANY

3. Tom Tetzlaff

   Forschungszentrum Jülich

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Strasse, Julich, Nordrhein-Westfalen, 52428, GERMANY

4. Rainer Waser

   Forschungszentrum Jülich

   Affiliation as printed

   Peter-Grünberg-Intsitute (PGI-7/10), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Strasse, Julich, Nordrhein-Westfalen, 52428, GERMANY

5. Regina Dittmann

   Forschungszentrum Jülich

   Affiliation as printed

   Peter-Grünberg-Institute (PGI-7), Forschungszentrum Julich GmbH, Wilhelm-Johnen-Strasse, Julich, Nordrhein-Westfalen, 52428, GERMANY

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

   RWTH Aachen University

   Affiliation as printed

   Institut für Werkstoffe der Elektrotechnik 2 (IWE2), Rheinisch-Westfälische Technische Hochschule Aachen, Templergraben 55, Aachen, Nordrhein-Westfalen, 52056, GERMANY