In-memory factorization of holographic perceptual representations
This addresses the challenge of efficient perceptual factorization for future AI systems, representing a novel hardware-software integration with potential domain-specific impact.
The paper tackles the problem of factorizing holographic perceptual representations, a critical task for AI perception, by presenting an in-memory compute engine that uses brain-inspired hyperdimensional computing and analog memristive devices, achieving the ability to solve problems at least five orders of magnitude larger than previously possible while reducing computational time and space complexity.
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.