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.

提出了一种完整的内存中超维计算（HDC）系统，该系统通过语言分类、新闻分类和电肌图信号手势识别三种典型的 HDC 相关学习任务，实验性地使用 760,000 个相变存储器件执行模拟内存中计算，实现了与软件实现相当的准确性。