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

22 papers, page 1 of 1

1. ICML 2025any4: Learned 4-bit Numeric Representation for LLMs
2. ICML 2025BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms
3. ICML 2025COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning
4. ICML 2025Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
5. ICML 2025Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability
6. ICML 2025Distilling the Knowledge in Data Pruning
7. ICML 2025Ensemble Distribution Distillation via Flow Matching
8. ICML 2025Fast Large Language Model Collaborative Decoding via Speculation
9. ICML 2025Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding
10. ICML 2025Matryoshka Quantization
11. ICML 2025Metadata Conditioning Accelerates Language Model Pre-training
12. ICML 2025On Exact Bit-level Reversible Transformers Without Changing Architecture
13. ICML 2025Optimizing Large Language Model Training Using FP4 Quantization
14. ICML 2025Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models
15. ICML 2025RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers
16. ICML 2025SADA: Stability-guided Adaptive Diffusion Acceleration
17. ICML 2025SageAttention2: Efficient Attention with Thorough Outlier Smoothing and Per-thread INT4 Quantization
18. ICML 2025Scalable Model Merging with Progressive Layer-wise Distillation
19. ICML 2025SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization
20. ICML 2025Sparsing Law: Towards Large Language Models with Greater Activation Sparsity
21. ICML 2025Streamline Without Sacrifice - Squeeze out Computation Redundancy in LMM
22. ICML 2025When Dynamic Data Selection Meets Data Augmentation: Achieving Enhanced Training Acceleration

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.