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TrainBridge is an agentic framework that transfers GPU compilation optimization guidance to Ascend NPU training programs, achieving an average 1.7× speedup across four workloads and nearly 2.0× across 27 Modded-NanoGPT speedrun records by extracting optimization intents from GPU executions and iteratively refining NPU programs through profiling.
The author explains operator fusion as a key mechanism behind torch.compile's speedups, and provides a minimal 500-line Python implementation and notebook as an educational tool.
This paper proposes an operator fusion strategy for LLM inference on Tenstorrent's Tensix architecture, fusing RMSNorm with matrix multiplications to improve data locality and reduce DRAM accesses. Experiments on the Wormhole platform with Qwen2.5-0.5B, Qwen3-0.6B, and Qwen3-4B show up to 37.44% latency reduction for attention and 15.89% for MLP.