Research map / Architectures and efficiency
Quantization: research map
646 accepted papers on Quantization in Architectures and efficiency, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 8 approaches. The busiest year so far is 2026.
Within Architectures and efficiency, its share grew from 6.0% in 2023–24 to 9.6% in 2025–26 (118 → 341 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
post quantization · ptq · compression · 413 papers
Approaches in this cluster:
- Post-training weight quantization (100 papers)
Compresses weights by quantization with sharpness control, loss-aware objectives, and quantization noise. - Post-training quantization of LLMs (136 papers)
Handles outliers and low-bit errors when quantizing large language models after training. - Post-training quantization of ViTs (105 papers)
Reduces quantization error in vision transformers using Hessian, Fisher, and outlier-aware reconstruction. - KV cache quantization (72 papers)
Compresses key-value caches with vector and mixed-precision quantization for long-context inference.
Most cited and most cited since 2024:
- QLoRA: Efficient Finetuning of Quantized LLMs (NeurIPS 2023 · 824 citations)
- Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations (NeurIPS 2017 · 358 citations)
- OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models (AAAI 2024 · 72 citations)
- KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization (NeurIPS 2024 · 51 citations)
mixed precision · floating · point · 233 papers
Approaches in this cluster:
- Mixed-precision quantization (92 papers)
Assigns bit-widths per layer for low-bit network training and inference with learnable quantizers. - Low-precision training formats (58 papers)
Designs 8-bit floating-point formats and accumulation schemes for low-precision neural network training. - Ultra-low-bit LLM quantization (56 papers)
Pushes language models to FP4, 1-bit, and multi-bit formats with scaling-law analysis. - Binary neural networks (27 papers)
Binarizes weights and activations with information-retention and ensemble techniques.
Most cited and most cited since 2024:
- Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference (CVPR 2018 · 3,940 citations)
- Mixed Precision Training (ICLR 2018 · 999 citations)
- JointSQ: Joint Sparsification-Quantization for Distributed Learning (CVPR 2024 · 18 citations)
- Towards Efficient Verification of Quantized Neural Networks (AAAI 2024 · 17 citations)
Related topics in Architectures and efficiency
- Pruning and efficient tuning (1,147)
- Attention and transformers (2,296)
- Neural architecture search (380)
- RNNs and normalization (2,827)
- Spiking networks (289)
- Mixture of experts (300)
- CNN design (853)