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.

H3DFact is presented as a high-dimensional holographic vector-based engine that efficiently factors sensory signals, demonstrating superior accuracy, operational capacity, compute density, energy efficiency, and silicon footprint compared to 2D designs.