# In-memory factorization of holographic perceptual representations

arXiv: https://arxiv.org/abs/2211.05052 · HTML: https://mlsanity.com/paper/2211.05052/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 60 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [RAVEN: A Dataset for Relational and Analogical Visual rEasoNing](https://mlsanity.com/paper/1903.02741/) (2019)
- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations](https://mlsanity.com/paper/2111.06077/) (2021)
- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021)
- [A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices](https://mlsanity.com/paper/2203.04571/) (2022)
- [Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models](https://mlsanity.com/paper/2202.04557/) (2022)
- [Integer Factorization with Compositional Distributed Representations](https://mlsanity.com/paper/2203.00920/) (2022)

## Built on by (most cited first, 10 of 10)

- [Learning from Hypervectors: A Survey on Hypervector Encoding](https://mlsanity.com/paper/2308.00685/) (2023)
- [Computing with Residue Numbers in High-Dimensional Representation](https://mlsanity.com/paper/2311.04872/) (2023)
- [Factorizers for Distributed Sparse Block Codes](https://mlsanity.com/paper/2303.13957/) (2023)
- [Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks](https://mlsanity.com/paper/2404.19126/) (2024)
- [Self-Attention Based Semantic Decomposition in Vector Symbolic Architectures](https://mlsanity.com/paper/2403.13218/) (2024)
- [H3DFact: Heterogeneous 3D Integrated CIM for Factorization with Holographic Perceptual Representations](https://mlsanity.com/paper/2404.04173/) (2024)
- [HyperCam: Low-Power Onboard Computer Vision for IoT Cameras](https://mlsanity.com/paper/2501.10547/) (2025)
- [On the Role of Noise in Factorizers for Disentangling Distributed Representations](https://mlsanity.com/paper/2412.00354/) (2024)
- [FactorHD: A Hyperdimensional Computing Model for Multi-Object Multi-Class Representation and Factorization](https://mlsanity.com/paper/2507.12366/) (2025)
- [Random resistive memory-based deep extreme point learning machine for unified visual processing](https://mlsanity.com/paper/2312.09262/) (2023)

## Abstract (arXiv)

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.