We introduce a parallelizable simplification of Neural Turing Machine (NTM), referred to as P-NTM, which redesigns the core operations of the original architecture to enable efficient scan-based parallel execution. We evaluate the proposed architecture on a synthetic benchmark of algorithmic problems involving state tracking, memorization, and basic arithmetic, solved via autoregressive decoding. We compare it against a revisited stable implementation of the standard NTM, as well as conventional recurrent and attention-based architectures. Results show that, despite its simplifications, the proposed model attains length generalization performance comparable to the original, learning to solve all problems, including unseen sequence lengths, with perfect accuracy. It also improves training efficiency, with parallel execution of P-NTM being up to an order of magnitude faster than the standard NTM. Ultimately, this work contributes toward the development of efficient neural architectures capable of expressing a broad class of algorithms.

翻译：本文提出了一种可并行化的神经图灵机简化版本，称为P-NTM。该模型重新设计了原始架构的核心操作，以实现基于扫描机制的高效并行执行。我们在一个涉及状态跟踪、记忆和基本算术的算法问题合成基准上，通过自回归解码的方式对所提出的架构进行了评估。我们将其与经过重新审视的、稳定的标准NTM实现，以及传统的循环和基于注意力的架构进行了比较。结果表明，尽管进行了简化，所提出的模型在长度泛化性能上与原版相当，能够以完美准确率学会解决所有问题，包括未见过的序列长度。同时，它提升了训练效率，P-NTM的并行执行速度比标准NTM快一个数量级。最终，这项工作有助于开发能够表达广泛算法类别的高效神经架构。