In-memory hyperdimensional computing
Geethan Karunaratne, Manuel Le Gallo, Giovanni Cherubini, Luca Benini, Abbas Rahimi
and Abu Sebastian
Nature Electronics (2020)
DOI: 10.1038/s41928-020-0410-3
Abstract: 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.
Fig.: The concept of in-memory hyperdimensional computing.
Acknowledgements: 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.
