English(EN) Flash RNN-T: Memory-Efficient Joint Network and Transducer Loss Computation

Flash RNN-T 提升了 NVIDIA H100 上的语音识别效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了 Flash RNN-T，这是一种用于计算循环神经网络换能器 (RNN-T) 的新实现，可显著提高语音识别任务的内存效率和吞吐量。该方法利用专用内核和可选的平铺重计算来减少内存占用并提高处理速度，尤其是在 NVIDIA H100 等高性能硬件上。与现有基线相比，Flash RNN-T 在吞吐量和内存节省方面均取得了显著的进步，能够处理否则会超出内存容量的更长工作负载。 AI

影响这项新实现为语音识别模型提供了显著的内存和吞吐量改进，有可能加速研究和部署。

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arXiv cs.LG TIER_1 English(EN) · Mahmoud Ashraf · 2026-10-19 04:00

Flash RNN-T：内存高效的联合网络和换能器损失计算

arXiv:2610.23409v1 Announce Type: new Abstract: The recurrent neural network transducer (RNN-T) remains a practical choice for streaming speech recognition, but training requires large intermediate tensors. We present Flash RNN-T, a joint and loss implementation combining special…

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Flash RNN-T：内存高效的联合网络和换能器损失计算

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