Research2d ago
Apple Unlocks Parallel Training For Large RNNs
ParaRNN removes the sequential bottleneck of Recurrent Neural Networks, allowing them to be trained in parallel like Transformers. This architecture enables RNNs to scale to billions of parameters while maintaining their inherent memory efficiency. Practitioners can now deploy high-capacity models on resource-constrained hardware without the compute overhead of attention mechanisms.