推理加速论文趋势 2023–2026

主要结论

• 推理加速论文从 2023 年的 54 篇增长到 2026 年的 91 篇，三年间扩大到约 1.7 倍。
• NeurIPS 是发推理加速论文最多的会议，2023–2026 年共 90 篇，占四个会议总量的 35%。
• 交叉方向里涨得最快的是深度学习 · large language models：同时属于两个主题的论文从 2023 年的 1 篇增至 2026 年的 9 篇。
• 在「效率与系统」的 5 个主题里，它的 2026 年论文数排第 5；同组增长最快的硬件协同是 4.4 倍，推理加速是 1.7 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026A Method-Class Divide in Sub-4-Bit Quantization:\\Iterative vs.\ Single-Pass Sensitivity to Data Composition
• ICML 2026Consistency Deep Equilibrium Models
• ICLR 2026OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
• CVPR 2026GeoRK2 Geometry-Guided Runge-Kutta Integration for Diffusion Transformer Acceleration
• NeurIPS 2025PolarQuant: Leveraging Polar Transformation for Key Cache Quantization and Decoding Acceleration
• ICML 2025RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers
• ICLR 2025Progressive distillation induces an implicit curriculum
• CVPR 2025Adapting Dense Matching for Homography Estimation with Grid-based Acceleration
• NeurIPS 2024Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks
• ICML 2024Diversified Batch Selection for Training Acceleration
• ICLR 2024Embarrassingly Simple Dataset Distillation
• CVPR 2024Data-Free Quantization via Pseudo-label Filtering
• NeurIPS 2023Continuous-time Analysis of Anchor Acceleration
• ICML 2023Auxiliary Modality Learning with Generalized Curriculum Distillation
• ICLR 2023Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats
• CVPR 2023NIPQ: Noise Proxy-Based Integrated Pseudo-Quantization
• NeurIPS 2026AcceleGrad#: Adaptive Geometry-Aware Acceleration
• ICML 2026PsumQuant: In-line Post-training Partial Sum Quantizer for Energy Efficient NPU Inference
• ICLR 2026NLI : Non-uniform Linear Interpolation Approximation of Nonlinear Operations for Efficient LLMs Inference
• CVPR 2026SODA Sensitivity-Oriented Dynamic Acceleration for Diffusion Transformer

全部推理加速论文

共 26 篇，第 1 / 1 页

1. ICML 2026ASTRA: Communication-Efficient Acceleration for Multi-Device Transformer Inference
2. ICML 2026Attn-QAT: 4-Bit Attention With Quantization-Aware Training
3. ICML 2026Budget-Constrained Step-Level Diffusion Caching
4. ICML 2026CoCoQuant: Breaking the Bandwidth Wall via Co-Optimized Communication and Computation Quantization
5. ICML 2026Consistency Deep Equilibrium Models
6. ICML 2026DC-Leap: Training-Free Acceleration of dLLMs via Draft-Guided Contiguous Leaping Decoding
7. ICML 2026FAFO: Lossy KV Cache Compression for Lossless Inference Acceleration via Draftless Fumble Decoding
8. ICML 2026FasterVAR: Plug-and-Play Acceleration for Visual Autoregressive Models
9. ICML 2026Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs
10. ICML 2026Improving Classifier-Free Guidance of Flow Matching via Manifold Projection
11. ICML 2026Inference of Online Newton Methods with Nesterov's Accelerated Sketching
12. ICML 2026LearniBridge: Learnable Calibration of Feature Caching for Diffusion Models Acceleration
13. ICML 2026Lie-Algebraic Acceleration of Neural Koopman Dynamics
14. ICML 2026MAnchors: Memorization-Based Acceleration of Anchors via Rule Reuse and Transformation
15. ICML 2026MAPS: Memory-Aware Predictive Scheduling Framework for Large Language Model Serving
16. ICML 2026Matrix-Free GPU Semidefinite Programming for Quantum Ordered Search at the k=6 Frontier
17. ICML 2026PsumQuant: In-line Post-training Partial Sum Quantizer for Energy Efficient NPU Inference
18. ICML 2026Pushing the Limits of Block Rotations in Post-Training Quantization
19. ICML 2026RSTR: Reducing SpatioTemporal Redundancy in Diffusion Transformers
20. ICML 2026RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models
21. ICML 2026Semantic Cache Distillation: Efficient State Transfer via Reuse and Selective Patching
22. ICML 2026SparseInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation
23. ICML 2026UniSVQ: 2-bit Unified Scalar-Vector Quantization
24. ICML 2026WaterSIC: Information-Theoretically (Near) Optimal \\Linear Layer Quantization
25. ICML 2026WAVE: Window-Aware Vocabulary-Efficient Early-Exit for Training-Free LLM Acceleration
26. ICML 2026XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding

常见问题

2023–2026 年四大会议一共有多少篇推理加速论文？

共 259 篇，按年份分别是 2023 年 54 篇、2024 年 50 篇、2025 年 64 篇、2026 年 91 篇。

NeurIPS 2026 有多少篇推理加速论文？

NeurIPS 2026 有 33 篇推理加速论文，上一届是 15 篇。

ICLR 2026 有多少篇推理加速论文？

ICLR 2026 有 17 篇推理加速论文，上一届是 18 篇。

ICML 2026 有多少篇推理加速论文？

ICML 2026 有 26 篇推理加速论文，上一届是 22 篇。

CVPR 2026 有多少篇推理加速论文？

CVPR 2026 有 15 篇推理加速论文，上一届是 9 篇。

哪个会议的推理加速论文最多？

NeurIPS，2023–2026 年共 90 篇。