Leading experts from both communities have suggested the need to (re)connect research in neuroscience and artificial intelligence (AI) to accelerate the development of next-generation AI innovations. They term this convergence as NeuroAI. Previous research has established temporal neural networks (TNNs) as a promising neuromorphic approach toward biological intelligence and efficiency. We fully embrace NeuroAI and propose a new category of TNNs we call NeuroAI TNNs (NeuTNNs) with greater capability and hardware efficiency by adopting neuroscience findings, including a neuron model with active dendrites and a hierarchy of distal and proximal segments. This work introduces a PyTorch-to-layout tool suite (NeuTNNGen) to design application-specific NeuTNNs. Compared to previous TNN designs, NeuTNNs achieve superior performance and efficiency. We demonstrate NeuTNNGen's capabilities using three example applications: 1) UCR time series benchmarks, 2) MNIST design exploration, and 3) Place Cells design for neocortical reference frames. We also explore using synaptic pruning to further reduce synapse counts and hardware costs by 30-50% while maintaining model precision across diverse sensory modalities. NeuTNNGen can facilitate the design of application-specific energy-efficient NeuTNNs for the next generation of NeuroAI computing systems.

翻译：来自两个领域的顶尖专家提出，有必要（重新）建立神经科学与人工智能（AI）研究之间的联系，以加速下一代AI创新的发展。他们将这种融合称为NeuroAI。先前的研究已确立时序神经网络（TNNs）作为一种实现生物智能与高效性的有前景的神经形态方法。我们完全接纳NeuroAI的理念，并提出一类新型TNNs，我们称之为NeuroAI TNNs（NeuTNNs）。通过采纳神经科学的研究成果——包括采用具有主动树突以及远端与近端层次化分段的神经元模型——NeuTNNs具备更强的能力和更高的硬件效率。本工作介绍了一个PyTorch到硬件布局的工具套件（NeuTNNGen），用于设计面向特定应用的NeuTNNs。与以往的TNN设计相比，NeuTNNs实现了更优的性能和效率。我们通过三个示例应用展示了NeuTNNGen的能力：1）UCR时间序列基准测试，2）MNIST设计探索，以及3）用于新皮层参考系的位置细胞设计。我们还探索了使用突触剪枝技术，在保持模型在不同感觉模态下精度的同时，进一步将突触数量和硬件成本降低30-50%。NeuTNNGen能够促进为下一代NeuroAI计算系统设计面向特定应用的高能效NeuTNNs。