1. In-memory factorization of holographic perceptual representations
- Author
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Langenegger, Jovin, Karunaratne, Geethan, Hersche, Michael, Benini, Luca, Sebastian, Abu, and Rahimi, Abbas
- Subjects
FOS: Computer and information sciences ,In-memory computing, Vector-symbolic Architecture, Resonator Networks ,Computer Science - Machine Learning ,Computer Vision and Pattern Recognition (cs.CV) ,Biomedical Engineering ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Emerging Technologies ,Computer Science - Neural and Evolutionary Computing ,Bioengineering ,Condensed Matter Physics ,Atomic and Molecular Physics, and Optics ,Machine Learning (cs.LG) ,Emerging Technologies (cs.ET) ,General Materials Science ,Neural and Evolutionary Computing (cs.NE) ,Electrical and Electronic Engineering - Abstract
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., 23 pages, 4 figures, 1 extended data figure, 3 supplementary notes, 2 supplementary figures and 3 supplementary tables
- Published
- 2022