In-memory hyperdimensional computing

By Geethan Karunaratne, M. Le Gallo, Giovanni Cherubini, Luca Benini, Abbas Rahimi, Abu Sebastian. 2019

305 citations recorded by the source corpus.

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. A complete in-memory hyperdimensional computing system, which uses 760,000 phase-change memory devices, can efficiently perform machine learning related tasks including language classification, news classification and hand gesture recognition from electromyography signals.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Constrained Few-shot Class-incremental Learning (2022, positive): “Furthermore, the precision of such robust prototypes can be reduced, as confirmed by the bipolarization in Mode 2, which makes them ideal for implementation on emerging hardware technologies exploiting non-volatile memory for in-memory computation [20,21].”
Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks (2022, background): “The dimensionality of support vector is typically set to thousands [13] to generate a holographic distributed representation that is extremely robust against nonidealities in in-memory computing [14].”
Memristive technologies for data storage, computation, encryption, and radio-frequency communication (2022, methodology): “This soft-reading type of associative memory search capability is used in several learning frameworks, such as hyperdimensional computing ( 71 ) and memory-augmented neural networks ( 72 ).”