NeuroAI Temporal Neural Networks (NeuTNNs): Microarchitecture and Design Framework for Specialized Neuromorphic Processing Units
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914300519514112 |
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| author | Venkatachalam, Shanmuga Vellaisamy, Prabhu Nair, Harideep Huang, Wei-Che Na, Youngseok Kang, Yuyang Jacobson, Quinn Shen, John Paul |
| author_facet | Venkatachalam, Shanmuga Vellaisamy, Prabhu Nair, Harideep Huang, Wei-Che Na, Youngseok Kang, Yuyang Jacobson, Quinn Shen, John Paul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01546 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NeuroAI Temporal Neural Networks (NeuTNNs): Microarchitecture and Design Framework for Specialized Neuromorphic Processing Units Venkatachalam, Shanmuga Vellaisamy, Prabhu Nair, Harideep Huang, Wei-Che Na, Youngseok Kang, Yuyang Jacobson, Quinn Shen, John Paul Hardware Architecture 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. |
| title | NeuroAI Temporal Neural Networks (NeuTNNs): Microarchitecture and Design Framework for Specialized Neuromorphic Processing Units |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2602.01546 |