  Hyperdimensional computing (HDC) is an emerging computational framework that
takes inspiration from attributes of neuronal circuits such as
hyperdimensionality, fully distributed holographic representation, and
(pseudo)randomness. When employed for machine learning tasks such as learning
and classification, HDC involves manipulation and comparison of large patterns
within memory. Moreover, a key attribute of HDC 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
paradigms such as in-memory computing, where the physical attributes of
nanoscale memristive devices are exploited to perform computation in place.
Here, we present a complete in-memory HDC system that achieves a near optimum
trade-off between design complexity and classification accuracy based on three
prototypical HDC related learning tasks, namely, language classification, news
classification, and hand gesture recognition from electromyography signals.
Comparable accuracies to software implementations are demonstrated,
experimentally, using 760,000 phase-change memory devices performing analog
in-memory computing.
