Artificial intelligence faces two growing challenges: computational bottlenecks and energy inefficiency inherent to the von Neumann architecture. Neuromorphic computing offers a promising alternative, yet its immature ecosystem still relies on conventional systems for data supply, requiring efficient conversion between tensor and spike dataflow. We observe a clear mismatch, where data conversion is slow while downstream computation is fast. As data size grows quadratically with large language models, this mismatch becomes a major bottleneck. In this work, we examine the data-conversion components that appear on the critical path of training and inference in neuromorphic LLMs. The work presents FlashT2S (Flash Tensor-to-Spike): GPU-Accelerated Tensor-to-Spike Conversion Operators, which applies architecture-aware and data-centric optimizations to reduce conversion latency. Experimental results show that FlashT2S delivers an average speedup of 850.78x over the native operators across individual operators and 802.83x in realistic workloads. Additionally, compared with GPU kernels automatically generated by torch.compile, our kernels achieve 5-11x speedups. Notably, it achieves near-optimal absolute performance: large-scale conversions are reduced to tens of microseconds (10-99 us), while small-scale conversions reach single-digit microsecond levels (less than 10 us). Furthermore, its standardized output enables seamless integration across heterogeneous backends, including diverse neuromorphic hardware.

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