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：感官信号构成因素的解离是感知和认知的核心，因此是未来人工智能系统的关键任务。本文提出一种计算引擎，通过利用类脑超维度计算的叠加计算能力，以及基于纳米级忆阻器件的模拟内存计算固有的随机性，高效分解全息感知表征。这种迭代式内存分解器能够解决至少五个数量级规模的、其他方法无法解决的问题，同时显著降低计算时间和空间复杂度。我们采用基于相变忆阻器件的两个内存计算芯片，对该分解器进行了大规模实验验证。其中主导的矩阵-向量乘法运算以O(1)复杂度执行，从而将计算时间复杂度降低至仅与迭代次数相关。此外，我们通过实验证明该分解器能够可靠且高效地分解视觉感知表征。