Practical Lessons on Vector-Symbolic Architectures in Deep Learning-Inspired Environments

Francesco S. Carzaniga, Michael Hersche, Kaspar Schindler, Abbas Rahimi
Proceedings of The 19th International Conference on Neurosymbolic Learning and Reasoning, PMLR 284:218-236, 2025.

Abstract

Neural networks have shown unprecedented capabilities, rivaling human performance in many tasks. However, current neural architectures are not capable of symbolic manipulation, which is thought to be a hallmark of human intelligence. Vector-symbolic architectures (VSAs) promise to bring this ability through simple vector manipulation, highly amenable to current and emerging hardware and software stacks built for their neural counterparts. Integrating the two models into the paradigm of neuro-vector-symbolic architectures may achieve even more human-like performance. However, despite ongoing efforts, there are no clear guidelines on the deployment of VSA in deep learning-based training situations. In this work, we aim to begin providing such guidelines by offering four practical lessons we have observed through the analysis of many VSA models and implementations. We provide thorough benchmarks and results that corroborate such lessons. First, we observe that Multiply-add-permute (MAP) and Hadamard linear binding (HLB) are up to 3-4$\times$ faster than holographic reduced representations (HRR), even when the latter is equipped with optimized FFT-based convolutions. Second, we propose further speed improvements by replacing similarity search with a linear readout, with no effect on retrieval. Third, we analyze the retrieval performance of MAP, HRR and HLB in a noise-free and noisy scenario to simulate processing by a neural network, and show that they are equivalent. Finally, we implement a hierarchical multi-level composition scheme, with notable benefits to the flexibility of integration of VSAs inside existing neural architectures. Overall, we show that these four lessons lead to faster and more effective deployment of VSA.

Cite this Paper

BibTeX
@InProceedings{pmlr-v284-carzaniga25a, title = {Practical Lessons on Vector-Symbolic Architectures in Deep Learning-Inspired Environments}, author = {Carzaniga, Francesco S. and Hersche, Michael and Schindler, Kaspar and Rahimi, Abbas}, booktitle = {Proceedings of The 19th International Conference on Neurosymbolic Learning and Reasoning}, pages = {218--236}, year = {2025}, editor = {H. Gilpin, Leilani and Giunchiglia, Eleonora and Hitzler, Pascal and van Krieken, Emile}, volume = {284}, series = {Proceedings of Machine Learning Research}, month = {08--10 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v284/main/assets/carzaniga25a/carzaniga25a.pdf}, url = {https://proceedings.mlr.press/v284/carzaniga25a.html}, abstract = {Neural networks have shown unprecedented capabilities, rivaling human performance in many tasks. However, current neural architectures are not capable of symbolic manipulation, which is thought to be a hallmark of human intelligence. Vector-symbolic architectures (VSAs) promise to bring this ability through simple vector manipulation, highly amenable to current and emerging hardware and software stacks built for their neural counterparts. Integrating the two models into the paradigm of neuro-vector-symbolic architectures may achieve even more human-like performance. However, despite ongoing efforts, there are no clear guidelines on the deployment of VSA in deep learning-based training situations. In this work, we aim to begin providing such guidelines by offering four practical lessons we have observed through the analysis of many VSA models and implementations. We provide thorough benchmarks and results that corroborate such lessons. First, we observe that Multiply-add-permute (MAP) and Hadamard linear binding (HLB) are up to 3-4$\times$ faster than holographic reduced representations (HRR), even when the latter is equipped with optimized FFT-based convolutions. Second, we propose further speed improvements by replacing similarity search with a linear readout, with no effect on retrieval. Third, we analyze the retrieval performance of MAP, HRR and HLB in a noise-free and noisy scenario to simulate processing by a neural network, and show that they are equivalent. Finally, we implement a hierarchical multi-level composition scheme, with notable benefits to the flexibility of integration of VSAs inside existing neural architectures. Overall, we show that these four lessons lead to faster and more effective deployment of VSA.} }
Endnote
%0 Conference Paper %T Practical Lessons on Vector-Symbolic Architectures in Deep Learning-Inspired Environments %A Francesco S. Carzaniga %A Michael Hersche %A Kaspar Schindler %A Abbas Rahimi %B Proceedings of The 19th International Conference on Neurosymbolic Learning and Reasoning %C Proceedings of Machine Learning Research %D 2025 %E Leilani H. Gilpin %E Eleonora Giunchiglia %E Pascal Hitzler %E Emile van Krieken %F pmlr-v284-carzaniga25a %I PMLR %P 218--236 %U https://proceedings.mlr.press/v284/carzaniga25a.html %V 284 %X Neural networks have shown unprecedented capabilities, rivaling human performance in many tasks. However, current neural architectures are not capable of symbolic manipulation, which is thought to be a hallmark of human intelligence. Vector-symbolic architectures (VSAs) promise to bring this ability through simple vector manipulation, highly amenable to current and emerging hardware and software stacks built for their neural counterparts. Integrating the two models into the paradigm of neuro-vector-symbolic architectures may achieve even more human-like performance. However, despite ongoing efforts, there are no clear guidelines on the deployment of VSA in deep learning-based training situations. In this work, we aim to begin providing such guidelines by offering four practical lessons we have observed through the analysis of many VSA models and implementations. We provide thorough benchmarks and results that corroborate such lessons. First, we observe that Multiply-add-permute (MAP) and Hadamard linear binding (HLB) are up to 3-4$\times$ faster than holographic reduced representations (HRR), even when the latter is equipped with optimized FFT-based convolutions. Second, we propose further speed improvements by replacing similarity search with a linear readout, with no effect on retrieval. Third, we analyze the retrieval performance of MAP, HRR and HLB in a noise-free and noisy scenario to simulate processing by a neural network, and show that they are equivalent. Finally, we implement a hierarchical multi-level composition scheme, with notable benefits to the flexibility of integration of VSAs inside existing neural architectures. Overall, we show that these four lessons lead to faster and more effective deployment of VSA.
APA
Carzaniga, F.S., Hersche, M., Schindler, K. & Rahimi, A.. (2025). Practical Lessons on Vector-Symbolic Architectures in Deep Learning-Inspired Environments. Proceedings of The 19th International Conference on Neurosymbolic Learning and Reasoning, in Proceedings of Machine Learning Research 284:218-236 Available from https://proceedings.mlr.press/v284/carzaniga25a.html.

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