H3DFact: Heterogeneous 3D Integrated CIM for Factorization with Holographic Perceptual Representations
This addresses the challenge of efficient holographic factorization for neuro-symbolic AI systems, representing an incremental hardware optimization.
The paper tackles the problem of factorizing high-dimensional holographic representations for perception and reasoning by proposing H3DFact, a heterogeneous 3D integrated in-memory compute engine, which achieves up to five orders of magnitude improvement in operational capacity and significant gains in compute density, energy efficiency, and silicon footprint.
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.