Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication

Abstract

Boolean circuits of McCulloch-Pitts threshold gates are a classic model of neural computation studied heavily in the late 20th century as a model of general computation. Recent advances in large-scale neural computing hardware has made their practical implementation a near-term possibility. We describe a theoretical approach for multiplying two $N$ by $N$ matrices that integrates threshold gate logic with conventional fast matrix multiplication algorithms, that perform $O(N^{\omega })$ arithmetic operations for a positive constant $\omega <3$. Our approach converts such a fast matrix multiplication algorithm into a constant-depth threshold circuit with approximately $O(N^{\omega })$ gates. Prior to our work, it was not known whether the $\Theta (N^3)$-gate barrier for matrix multiplication was surmountable by constant-depth threshold circuits. Dense matrix multiplication is a core operation in convolutional neural network training. Performing this work on a neural architecture instead of off-loading it to a GPU may be an appealing option.

Appears in the proceedings of the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 2018

References in corpus (1)

• A Survey of Neuromorphic Computing and Neural Networks in Hardware

Cited by in corpus (1)

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