In-memory hyperdimensional computing


Hyperdimensional computing is an emerging computational framework that takes inspiration from attributes of neuronal circuits including hyperdimensionality, fully distributed holographic representation and (pseudo)randomness. When employed for machine learning tasks, such as learning and classification, the framework involves manipulation and comparison of large patterns within memory. A key attribute of hyperdimensional computing is its robustness to the imperfections associated with the computational substrates on which it is implemented. It is therefore particularly amenable to emerging non-von Neumann approaches such as in-memory computing, where the physical attributes of nanoscale memristive devices are exploited to perform computation. Here, we report a complete in-memory hyperdimensional computing system in which all operations are implemented on two memristive crossbar engines together with peripheral digital complementary metal–oxide–semiconductor (CMOS) circuits. Our approach can achieve a near-optimum trade-off between design complexity and classification accuracy based on three prototypical hyperdimensional computing-related learning tasks: language classification, news classification and hand gesture recognition from electromyography signals. Experiments using 760,000 phase-change memory devices performing analog in-memory computing achieve comparable accuracies to software implementations.

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Fig. 1: The concept of in-memory HDC.
Fig. 2: AM search.
Fig. 3: In-memory n-gram encoding based on 2-minterm.
Fig. 4: The complete in-memory HDC system.

Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.


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This work was supported in part by the European Research Council through the European Union’s Horizon 2020 Research and Innovation Programme under grant no. 682675 and in part by the European Union’s Horizon 2020 Research and Innovation Programme through the project MNEMOSENE under grant no. 780215.

Author information




All authors collectively conceived the idea of in-memory hyperdimensional computing. G.K. performed the experiments and analysed the results under the supervision of M.L.G., A.R. and A.S. G.K., M.L.G., A.R. and A.S. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Abbas Rahimi or Abu Sebastian.

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Extended data

Extended Data Table 1 Architecture configurations and hyperparameters used for the tree different tasks
Extended Data Table 2 Parameters for PCM crossbars energy and area estimation

Supplementary information

Supplementary Information

Supplementary Notes 1–6.

Supplementary Video 1

Experimental demonstration of language recognition with in-memory hyperdimensional computing implemented in the phase-change memory hardware platform.

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Karunaratne, G., Le Gallo, M., Cherubini, G. et al. In-memory hyperdimensional computing. Nat Electron 3, 327–337 (2020).

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