Inference Acceleration papers, 2023–2026

Key findings

• Inference Acceleration went from 54 papers in 2023 to 91 in 2026, roughly 1.7x in three years.
• NeurIPS publishes the most Inference Acceleration work: 90 papers from 2023 to 2026, 35% of the four-venue total.
• The fastest-growing overlap is with Deep learning · large language models: papers tagged with both rose from 1 in 2023 to 9 in 2026.
• Among the 5 topics under Efficiency and Systems, it ranks #5 by 2026 paper count; the fastest grower in the group, Hardware-Aware ML, is up 4.4x versus 1.7x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Inference Acceleration papers

53 papers, page 1 of 2

1. CVPR 2026Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration
2. CVPR 2026D2Cache Second-Order Delta Caching for Higher Video Diffusion Acceleration
3. CVPR 2026Denoising as Path Planning Training-Free Acceleration of Diffusion Models with
4. CVPR 2026Fast3Dcache Training-free 3D Geometry Synthesis Acceleration
5. CVPR 2026Flow Map Distillation Without Data
6. CVPR 2026GeoRK2 Geometry-Guided Runge-Kutta Integration for Diffusion Transformer Acceleration
7. CVPR 2026Just-in-Time Training-Free Spatial Acceleration for Diffusion Transformers
8. CVPR 2026LESA Learnable Stage-Aware Predictors for Diffusion Model Acceleration
9. CVPR 2026Masked Auto-Regressive Variational Acceleration Fast Inference Makes Practical Reinforcement Learning
10. CVPR 2026ResCa Residual Caching for Diffusion Transformers Acceleration
11. CVPR 2026S2D Selective Spectral Decay for Quantization-Friendly Conditioning of Neural Activations
12. CVPR 2026Seele A Unified Acceleration Framework for Real-Time Gaussian Splatting on
13. CVPR 2026SODA Sensitivity-Oriented Dynamic Acceleration for Diffusion Transformer
14. CVPR 2026TAP A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration
15. CVPR 2026TC-Pade Trajectory-Consistent Pade Approximation for Diffusion Acceleration
16. CVPR 2025Adapting Dense Matching for Homography Estimation with Grid-based Acceleration
17. CVPR 2025Attend to Not Attended: Structure-then-Detail Token Merging for Post-training DiT Acceleration
18. CVPR 2025Efficient ANN-Guided Distillation: Aligning Rate-based Features of Spiking Neural Networks through Hybrid Block-wise Replacement
19. CVPR 2025HOT: Hadamard-based Optimized Training
20. CVPR 2025Instant Adversarial Purification with Adversarial Consistency Distillation
21. CVPR 2025Libra-Merging: Importance-redundancy and Pruning-merging Trade-off for Acceleration Plug-in in Large Vision-Language Model
22. CVPR 2025Quantization without Tears
23. CVPR 2025RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories
24. CVPR 2025SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation
25. CVPR 20243D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation
26. CVPR 2024Are Conventional SNNs Really Efficient? A Perspective from Network Quantization
27. CVPR 2024CLIP-KD: An Empirical Study of CLIP Model Distillation
28. CVPR 2024Data-Free Quantization via Pseudo-label Filtering
29. CVPR 2024Enhancing Post-training Quantization Calibration through Contrastive Learning
30. CVPR 2024Nearest is Not Dearest: Towards Practical Defense against Quantization-conditioned Backdoor Attacks
31. CVPR 2024One-step Diffusion with Distribution Matching Distillation
32. CVPR 2024PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor
33. CVPR 2024QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition
34. CVPR 2024Retraining-Free Model Quantization via One-Shot Weight-Coupling Learning
35. CVPR 2024Scaled Decoupled Distillation
36. CVPR 2024SDPose: Tokenized Pose Estimation via Circulation-Guide Self-Distillation
37. CVPR 2024Towards More Accurate Diffusion Model Acceleration with A Timestep Tuner
38. CVPR 2023Adaptive Data-Free Quantization
39. CVPR 2023Binary Latent Diffusion
40. CVPR 2023Boost Vision Transformer With GPU-Friendly Sparsity and Quantization
41. CVPR 2023DepGraph: Towards Any Structural Pruning
42. CVPR 2023DistilPose: Tokenized Pose Regression With Heatmap Distillation
43. CVPR 2023DisWOT: Student Architecture Search for Distillation WithOut Training
44. CVPR 2023Efficient RGB-T Tracking via Cross-Modality Distillation
45. CVPR 2023Generic-to-Specific Distillation of Masked Autoencoders
46. CVPR 2023Hard Sample Matters a Lot in Zero-Shot Quantization
47. CVPR 2023Network Expansion for Practical Training Acceleration
48. CVPR 2023NIPQ: Noise Proxy-Based Integrated Pseudo-Quantization
49. CVPR 2023On Distillation of Guided Diffusion Models
50. CVPR 2023PD-Quant: Post-Training Quantization Based on Prediction Difference Metric

FAQ

How many Inference Acceleration papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

259 in total: 54 in 2023, 50 in 2024, 64 in 2025, 91 in 2026.

How many Inference Acceleration papers are at NeurIPS 2026?

NeurIPS 2026 has 33 Inference Acceleration papers, versus 15 the year before.

How many Inference Acceleration papers are at ICLR 2026?

ICLR 2026 has 17 Inference Acceleration papers, versus 18 the year before.

How many Inference Acceleration papers are at ICML 2026?

ICML 2026 has 26 Inference Acceleration papers, versus 22 the year before.

How many Inference Acceleration papers are at CVPR 2026?

CVPR 2026 has 15 Inference Acceleration papers, versus 9 the year before.

Which conference publishes the most Inference Acceleration papers?

NeurIPS, with 90 papers from 2023 to 2026.