Disentangling the attributes of a sensory signal is central to sensory perception and cognition and hence is a critical task for future artificial intelligence systems. Here we present a compute engine capable of efficiently factorizing high-dimensional holographic representations of combinations of such attributes, by exploiting the computation-in-superposition capability of brain-inspired hyperdimensional computing, and the intrinsic stochasticity associated with analogue in-memory computing based on nanoscale memristive devices. Such an iterative in-memory factorizer is shown to solve at least five orders of magnitude larger problems that cannot be solved otherwise, as well as substantially lowering the computational time and space complexity. We present a large-scale experimental demonstration of the factorizer by employing two in-memory compute chips based on phase-change memristive devices. The dominant matrix–vector multiplication operations take a constant time, irrespective of the size of the matrix, thus reducing the computational time complexity to merely the number of iterations. Moreover, we experimentally demonstrate the ability to reliably and efficiently factorize visual perceptual representations.

理清感官信号的属性是感官知觉和认知的核心，因此是未来人工智能系统的一项关键任务。在这里，我们提出了一种计算引擎，能够通过利用类脑超维计算的叠加计算能力，以及与基于模拟内存计算相关的内在随机性，有效分解这些属性组合的高维全息表示。纳米级忆阻器件。这种迭代内存分解器被证明可以解决至少五个数量级的大问题，而这些问题无法通过其他方式解决，并且大大降低了计算时间和空间的复杂性。我们通过使用两个基于相变忆阻器件的内存计算芯片，展示了因式分解器的大规模实验演示。无论矩阵的大小如何，占主导地位的矩阵向量乘法运算都需要一个常数时间，从而将计算时间复杂度降低到仅为迭代次数。此外，我们通过实验证明了可靠有效地分解视觉感知表示的能力。