arXiv:2604.09558v2 Announce Type: replace-cross Abstract: With the widening gap between compute and memory operation latencies, data movement optimizations have become increasingly important for DNN compilation. Current optimizations such as layout transformations and operator fusion only target a subset of tensor operators and consequently miss important opportunities for reducing data movement in contemporary DNN workloads, including large language models. We introduce VTC, a novel tensor compilation framework that for the first time eliminates all unnecessary data movement by targeting the
The increasing gap between compute and memory operation latencies, especially with the rise of large language models, makes data movement the primary bottleneck in DNN compilation.
Improving DNN compilation efficiency and reducing data movement is crucial for scaling AI workloads, optimizing chip utilization, and driving down the operational costs of AI.
Traditional data movement optimizations are being superseded by more comprehensive tensor compilation frameworks capable of eliminating unnecessary data movement across all tensor operators.
- · AI compute infrastructure providers
- · Large language model developers
- · Chip manufacturers
- · Developers of AI compilation tools
- · Inefficient DNN compiler approaches
- · Data center operators with sub-optimal hardware utilization
More efficient and faster training and inference for deep neural networks.
Reduced energy consumption and operational costs for large-scale AI deployments.
Acceleration of AI model development and deployment across various industries due to better performance and resource utilization.
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Read at arXiv cs.LG