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In-memory factorization of holographic perceptual representations

Nature Nanotechnology volume 18, pages 479–485 (2023)Cite this article

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Abstract

Disentangling the attributes of a sensory signal is central to sensory perception and cognition and hence is a critical task for future artificial intelligence systems. Here we present a compute engine capable of efficiently factorizing high-dimensional holographic representations of combinations of such attributes, by exploiting the computation-in-superposition capability of brain-inspired hyperdimensional computing, and the intrinsic stochasticity associated with analogue in-memory computing based on nanoscale memristive devices. Such an iterative in-memory factorizer is shown to solve at least five orders of magnitude larger problems that cannot be solved otherwise, as well as substantially lowering the computational time and space complexity. We present a large-scale experimental demonstration of the factorizer by employing two in-memory compute chips based on phase-change memristive devices. The dominant matrix–vector multiplication operations take a constant time, irrespective of the size of the matrix, thus reducing the computational time complexity to merely the number of iterations. Moreover, we experimentally demonstrate the ability to reliably and efficiently factorize visual perceptual representations.

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Fig. 1: Factorization of perceptual representations using the in-memory factorizer.
Fig. 2: Stochastic similarity computation, sparse activations and limit cycles.
Fig. 3: Operational capacity of the stochastic in-memory factorizer with sparse activations.
Fig. 4: Experimental realization of the in-memory factorizer.

Data availability

The data that support the findings of this study are available via Zenodo at https://zenodo.org/record/7599430. Source data are provided with this paper.

Code availability

Our code is available via GitHub at https://github.com/IBM/in-memory-factorizer.

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Acknowledgements

This work is supported by the IBM Research AI Hardware Center and by the Swiss National Science Foundation (SNF) (grant no. 200800). We thank M. Le Gallo for the technical help; K. Brew and J. Li for assistance with TEM imaging of PCM devices; and V. Narayanan, C. Apte and R. Haas for managerial support.

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Authors and Affiliations

  1. IBM Research—Zurich, Rüschlikon, Switzerland

    Jovin Langenegger, Geethan Karunaratne, Michael Hersche, Abu Sebastian & Abbas Rahimi

  2. Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland

    Jovin Langenegger, Geethan Karunaratne, Michael Hersche & Luca Benini

Authors
  1. Jovin Langenegger
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  2. Geethan Karunaratne
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  3. Michael Hersche
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  4. Luca Benini
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  5. Abu Sebastian
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Contributions

J.L., G.K., M.H., A.S. and A.R. conceived the idea and designed the experiments. J.L. performed the experiments and characterization. J.L., A.S. and A.R. wrote the paper, with input from all the authors. All the authors provided critical comments and analyses.

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Correspondence to Abu Sebastian or Abbas Rahimi.

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The authors declare no competing interests.

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Nature Nanotechnology thanks Mario Lanza and Yuchao Yang for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Desirable range of noise.

The aggregated noise corresponding to the programming noise, drift variability, and read noise in the PCM devices affects (a) the accuracy of factorization, and (b) the number of iterations to converge. The optimal range for the standard deviation of the noise lies between 0.293μS and 1.277μS. As indicated by the green vertical line, the level of noise observed in the experimental crossbar array lies within the desirable range of noise.

Source data

Supplementary information

Supplementary Information

Supplementary Notes 1–4, Tables 1–3 and Figs. 1–5.

This video showcases one application of the proposed in-memory factorizer. Here the visual attributes of an image are disentangled using a front-end convolutional neural network and a back-end in-memory factorizer.

Source data

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Langenegger, J., Karunaratne, G., Hersche, M. et al. In-memory factorization of holographic perceptual representations. Nat. Nanotechnol. 18, 479–485 (2023). https://doi.org/10.1038/s41565-023-01357-8

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