ETO: Accelerating Optimization of DNN Operators by High-Performance Tensor Program Reuse
Summary: ETO accelerates DNN operator optimization via cross-operator tensor program reuse, with defined reuse conditions. A reuse-based tuner prunes search space and bridges to boost cross-operator reuse, interfacing with backends for fast, effective tuning. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jingzhi Fang
- 2. Yanyan Shen
- 3. Yue Wang
- 4. Lei Chen
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,326 | BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach | 2023 | SIGMOD | 4.3556432e-05 |
| 9,677 | Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving | 2025 | SIGMOD | 4.3047774e-05 |
| 9,806 | The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format | 2024 | SIGMOD | 4.2805224e-05 |
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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