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This is a Experimental Study study.

October 10, 2025Open Access

Single-Core Superscalar Optimization of Clifford Neural Layers

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Analysis reveals 21.35x speedup in inference process of Clifford neural layers, highlighting optimization techniques and equivariance properties.

A final average speedup of 21.35x over the baseline implementation was achieved with optimizations.
Performance was comparable to or exceeded the original PyTorch implementation in six functions.
Systematic application of optimization techniques enhanced performance while maintaining correctness.
Analyzing the theoretical foundations of Clifford algebras helped eliminate redundant computations.

Huang et al. (2025) studied this question.

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Analysis reveals 21.35x speedup in inference process of Clifford neural layers, highlighting optimization techniques and equivariance properties.