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

50 papers, page 1 of 1

1. CVPR 20243D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation
2. CVPR 2024Are Conventional SNNs Really Efficient? A Perspective from Network Quantization
3. CVPR 2024CLIP-KD: An Empirical Study of CLIP Model Distillation
4. CVPR 2024Data-Free Quantization via Pseudo-label Filtering
5. CVPR 2024Enhancing Post-training Quantization Calibration through Contrastive Learning
6. CVPR 2024Nearest is Not Dearest: Towards Practical Defense against Quantization-conditioned Backdoor Attacks
7. CVPR 2024One-step Diffusion with Distribution Matching Distillation
8. CVPR 2024PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor
9. CVPR 2024QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition
10. CVPR 2024Retraining-Free Model Quantization via One-Shot Weight-Coupling Learning
11. CVPR 2024Scaled Decoupled Distillation
12. CVPR 2024SDPose: Tokenized Pose Estimation via Circulation-Guide Self-Distillation
13. CVPR 2024Towards More Accurate Diffusion Model Acceleration with A Timestep Tuner
14. ICLR 2024Embarrassingly Simple Dataset Distillation
15. ICLR 2024InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning
16. ICML 2024A2Q+: Improving Accumulator-Aware Weight Quantization
17. ICML 2024Accelerating Transformer Pre-training with 2:4 Sparsity
18. ICML 2024Block Acceleration Without Momentum: On Optimal Stepsizes of Block Gradient Descent for Least-Squares
19. ICML 2024Data-free Distillation of Diffusion Models with Bootstrapping
20. ICML 2024Ditto: Quantization-aware Secure Inference of Transformers upon MPC
21. ICML 2024Diversified Batch Selection for Training Acceleration
22. ICML 2024Extreme Compression of Large Language Models via Additive Quantization
23. ICML 2024KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache
24. ICML 2024Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
25. ICML 2024Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique
26. ICML 2024Purifying Quantization-conditioned Backdoors via Layer-wise Activation Correction with Distribution Approximation
27. ICML 2024Residual Quantization with Implicit Neural Codebooks
28. ICML 2024Towards Efficient Spiking Transformer: a Token Sparsification Framework for Training and Inference Acceleration
29. NeurIPS 2024Accelerated Regularized Learning in Finite N-Person Games
30. NeurIPS 2024Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values
31. NeurIPS 2024Adaptive Depth Networks with Skippable Sub-Paths
32. NeurIPS 2024AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising
33. NeurIPS 2024BiDM: Pushing the Limit of Quantization for Diffusion Models
34. NeurIPS 2024Constant Acceleration Flow
35. NeurIPS 2024CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
36. NeurIPS 2024DeltaDEQ: Exploiting Heterogeneous Convergence for Accelerating Deep Equilibrium Iterations
37. NeurIPS 2024Fast Best-of-N Decoding via Speculative Rejection
38. NeurIPS 2024Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference
39. NeurIPS 2024GL-NeRF: Gauss-Laguerre Quadrature Enables Training-Free NeRF Acceleration
40. NeurIPS 2024KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization
41. NeurIPS 2024Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching
42. NeurIPS 2024Nesterov acceleration despite very noisy gradients
43. NeurIPS 2024OneBit: Towards Extremely Low-bit Large Language Models
44. NeurIPS 2024Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks
45. NeurIPS 2024ScaleKD: Strong Vision Transformers Could Be Excellent Teachers
46. NeurIPS 2024ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization
47. NeurIPS 2024Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration
48. NeurIPS 2024UniFL: Improve Latent Diffusion Model via Unified Feedback Learning
49. NeurIPS 2024Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction
50. NeurIPS 2024VQ-Map: Bird's-Eye-View Map Layout Estimation in Tokenized Discrete Space via Vector Quantization

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.