Disentanglement of constituent factors of a sensory signal is central to perception and cognition and hence is a critical task for future artificial intelligence systems. In this paper, we present a compute engine capable of efficiently factorizing holographic perceptual representations by exploiting the computation-in-superposition capability of brain-inspired hyperdimensional computing and the intrinsic stochasticity associated with analog 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, while also significantly 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 multiply operations are executed at O(1) thus reducing the computational time complexity to merely the number of iterations. Moreover, we experimentally demonstrate the ability to factorize visual perceptual representations reliably and efficiently.

翻译：感官信号构成要素的分解是感知和认知的核心,因此是未来人工智能系统的一项关键任务。在本文中,我们展示了一个计算引擎,它能够通过利用大脑激发的超维计算计算计算在超位中的能力,以及以纳米范围中间装置为基础的模拟内模计算产生的内在随机性,从而高效地将全息感官的感官表现因素化为因素。这种迭代内模因子显示至少能解决五级无法以其他方式解决的更大程度的问题,同时大大降低计算时间和空间复杂性。我们通过使用基于阶段改变中间装置的两种模拟微博芯,对因子进行大规模实验性示范。在O(1) 进行占主导地位的矩阵-变量倍增操作,从而将计算时间的复杂度降低到仅是迭代数。此外,我们实验性地展示了将视觉感官表现可靠和高效因素化的能力。