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.

超维计算是一种新兴的计算框架，它从神经元回路的属性中获得启发，这些属性包括超维，完全分布的全息表示和（伪）随机性。当用于机器学习任务（例如学习和分类）时，该框架将涉及内存中大型模式的操纵和比较。超维计算的关键属性是其对与实现它的计算基质相关的缺陷的鲁棒性。因此，特别适合新兴的非冯·诺依曼方法，例如内存计算，其中利用了纳米级忆阻器件的物理属性来执行计算。这里，我们报告了一个完整的内存超维计算系统，其中所有操作都在两个忆阻纵横开关引擎以及外围数字互补金属-氧化物-半导体（CMOS）电路上实现。我们的方法可以基于三种典型的与超维计算相关的学习任务，在设计复杂度和分类精度之间实现接近最佳的权衡：语言分类，新闻分类和肌电信号的手势识别。使用760,000个相变存储设备执行模拟内存计算的实验可获得与软件实现相当的精度。我们的方法可以基于三种典型的与超维计算相关的学习任务，在设计复杂度和分类精度之间实现接近最佳的权衡：语言分类，新闻分类和肌电信号的手势识别。使用760,000个相变存储设备执行模拟内存计算的实验可获得与软件实现相当的精度。我们的方法可以基于三种典型的与超维计算相关的学习任务，在设计复杂度和分类精度之间实现接近最佳的权衡：语言分类，新闻分类和肌电信号的手势识别。使用760,000个相变存储设备执行模拟内存计算的实验可获得与软件实现相当的精度。