硬件协同论文趋势 2023–2026

主要结论

• 硬件协同论文从 2023 年的 173 篇增长到 2026 年的 765 篇，三年间扩大到约 4.4 倍。
• CVPR 是发硬件协同论文最多的会议，2023–2026 年共 479 篇，占四个会议总量的 31%。
• 交叉方向里涨得最快的是扩散模型：同时属于两个主题的论文从 2023 年的 4 篇增至 2026 年的 58 篇。
• 在「效率与系统」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的数据集与基准是 2.1 倍，硬件协同是 4.4 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 2026AcceleGrad#: Adaptive Geometry-Aware Acceleration
• ICML 2026A Geometry-Aware Efficient Algorithm for Compositional Entropic Risk Minimization
• ICLR 2026Confident Block Diagonal Structure-Aware Invariable Graph Completion for Incomplete Multi-view Clustering
• CVPR 2026ActAvatar Temporally-Aware Precise Action Control for Talking Avatars
• NeurIPS 2025AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking
• ICML 2025CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging
• ICLR 2025FlashRNN: I/O-Aware Optimization of Traditional RNNs on modern hardware
• CVPR 2025A Distractor-Aware Memory for Visual Object Tracking with SAM2
• NeurIPS 2024HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models
• ICML 2024A Universal Class of Sharpness-Aware Minimization Algorithms
• ICLR 2024Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts
• CVPR 2024Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identification
• NeurIPS 2023Affinity-Aware Graph Networks
• ICML 2023A Three-regime Model of Network Pruning
• ICLR 2023$\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks
• CVPR 20233D-Aware Face Swapping
• NeurIPS 2026Accelerating Rectified Flow Models via Trajectory-Aware Caching
• ICML 2026Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse
• ICLR 2026Distribution-Aware Multi-Granularity Phase Coding: Towards Lower Conversion Error for Spike-Driven Large Language Models
• CVPR 2026BD-Merging Bias-Aware Dynamic Model Merging with Evidence-Guided Contrastive Learning

全部硬件协同论文

共 27 篇，第 1 / 1 页

1. ICML 2023A Three-regime Model of Network Pruning
2. ICML 2023Beyond In-Domain Scenarios: Robust Density-Aware Calibration
3. ICML 2023COLA: Orchestrating Error Coding and Learning for Robust Neural Network Inference Against Hardware Defects
4. ICML 2023Controllability-Aware Unsupervised Skill Discovery
5. ICML 2023DevFormer: A Symmetric Transformer for Context-Aware Device Placement
6. ICML 2023Differentially Private Sharpness-Aware Training
7. ICML 2023Discover and Cure: Concept-aware Mitigation of Spurious Correlation
8. ICML 2023Distortion and Uncertainty Aware Loss for Panoramic Depth Completion
9. ICML 2023Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape
10. ICML 2023Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming
11. ICML 2023Fair and Accurate Decision Making through Group-Aware Learning
12. ICML 2023Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
13. ICML 2023FedDisco: Federated Learning with Discrepancy-Aware Collaboration
14. ICML 2023Generalized Disparate Impact for Configurable Fairness Solutions in ML
15. ICML 2023Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing
16. ICML 2023Implicit Jacobian regularization weighted with impurity of probability output
17. ICML 2023Improving the Model Consistency of Decentralized Federated Learning
18. ICML 2023One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training
19. ICML 2023Phase-aware Adversarial Defense for Improving Adversarial Robustness
20. ICML 2023Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
21. ICML 2023R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents
22. ICML 2023Reachability-Aware Laplacian Representation in Reinforcement Learning
23. ICML 2023SAAL: Sharpness-Aware Active Learning
24. ICML 2023Simple Hardware-Efficient Long Convolutions for Sequence Modeling
25. ICML 2023SpeedDETR: Speed-aware Transformers for End-to-end Object Detection
26. ICML 2023Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks
27. ICML 2023Symmetry-Aware Robot Design with Structured Subgroups

常见问题

2023–2026 年四大会议一共有多少篇硬件协同论文？

共 1,524 篇，按年份分别是 2023 年 173 篇、2024 年 263 篇、2025 年 323 篇、2026 年 765 篇。

NeurIPS 2026 有多少篇硬件协同论文？

NeurIPS 2026 有 263 篇硬件协同论文，上一届是 120 篇。

ICLR 2026 有多少篇硬件协同论文？

ICLR 2026 有 120 篇硬件协同论文，上一届是 44 篇。

ICML 2026 有多少篇硬件协同论文？

ICML 2026 有 186 篇硬件协同论文，上一届是 72 篇。

CVPR 2026 有多少篇硬件协同论文？

CVPR 2026 有 196 篇硬件协同论文，上一届是 87 篇。

哪个会议的硬件协同论文最多？

CVPR，2023–2026 年共 479 篇。