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October 16, 2025Open Access

Recurrent Deep Differentiable Logic Gate Networks

Key Points

RDDLGN outperformed traditional methods with 5.00 BLEU accuracy in sequence learning, showcasing effectiveness.
During training, the system achieved 30.9% accuracy, nearing GRU's performance metrics for language translation.
This paper introduces a novel architecture combining differentiable logic gates with recurrent models for improved outcomes.
The findings suggest significant potential for FPGA acceleration in neural computation within sequential modeling.

Abstract

While differentiable logic gates have shown promise in feedforward networks, their application to sequential modeling remains unexplored. This paper presents the first implementation of Recurrent Deep Differentiable Logic Gate Networks (RDDLGN), combining Boolean operations with recurrent architectures for sequence-to-sequence learning. Evaluated on WMT'14 English-German translation, RDDLGN achieves 5.00 BLEU and 30.9\% accuracy during training, approaching GRU performance (5.41 BLEU) and graceful degradation (4.39 BLEU) during inference. This work establishes recurrent logic-based neural computation as viable, opening research directions for FPGA acceleration in sequential modeling and other recursive network architectures.

Recurrent Deep Differentiable Logic Gate Networks

Key Points

Abstract

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