Disentangling attributes of various sensory signals is central to human-like perception and reasoning and a critical task for higher-order cognitive and neuro-symbolic AI systems. An elegant approach to represent this intricate factorization is via high-dimensional holographic vectors drawing on brain-inspired vector symbolic architectures. However, holographic factorization involves iterative computation with high-dimensional matrix-vector multiplications and suffers from non-convergence problems. In this paper, we present H3DFact, a heterogeneous 3D integrated in-memory compute engine capable of efficiently factorizing high-dimensional holographic representations. H3DFact exploits the computation-in-superposition capability of holographic vectors and the intrinsic stochasticity associated with memristive-based 3D compute-in-memory. Evaluated on large-scale factorization and perceptual problems, H3DFact demonstrates superior capability in factorization accuracy and operational capacity by up to five orders of magnitude, with 5.5x compute density, 1.2x energy efficiency improvements, and 5.9x less silicon footprint compared to iso-capacity 2D designs.

翻译：解开各种感官信号中的属性特征是人类感知与推理的核心能力，也是高阶认知及神经符号AI系统的关键任务。受大脑启发的向量符号架构采用高维全息向量为解决这一复杂分解问题提供了优雅方法。然而，全息分解涉及高维矩阵-向量乘法的迭代计算，且存在非收敛问题。本文提出H3DFact——一种能够高效分解高维全息表示的异构3D集成存内计算引擎。该引擎利用全息向量的叠加态计算能力以及忆阻器基3D存算一体架构的固有随机性。在大规模分解与感知任务上的评估表明，与同等容量的2D设计相比，H3DFact在分解精度与运算容量上展现出高达五个数量级的优势，同时实现了5.5倍的计算密度提升、1.2倍的能效改善以及5.9倍的硅片面积缩减。