Recurrent Deep Differentiable Logic Gate Networks
It establishes recurrent logic-based neural computation as viable, potentially enabling FPGA acceleration for sequential modeling, though it appears incremental as it extends existing differentiable logic gate methods to recurrent architectures.
This paper tackled the problem of applying differentiable logic gates to sequential modeling by introducing Recurrent Deep Differentiable Logic Gate Networks (RDDLGN) for sequence-to-sequence learning, achieving 5.00 BLEU on WMT'14 English-German translation, which approaches GRU performance.
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.