Activation Quantization

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12 papers in the last four weeks, up 140% on the four weeks before. 0.1% of all new papers.

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ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA
May 3, 2026Shengzhe Lyu, Yuhan She, Patrick S. Y. Hung +2Efficient Neural Network Inference Edge Inference
City University of Hong Kong, Hong Kong
Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay
May 2, 2026Jin Tong, Guang Liang, Peilin Sun +1Vision Transformer Activation Outliers
State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210023, China · School of Artificial Intelligence, Nanjing University, Nanjing 210023, China · Zhongguancun Academy, Beijing 100094, China
AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs
May 1, 2026Wenxiang Lin, Juntao Huang, Luhan Zhang +5LLM Quantization Communication-Efficient Distributed Training
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen · Huawei Technologies Ltd.
BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs
May 1, 2026Zhixiong Zhao, Zukang Xu, Dawei YangLLM Quantization LLM Inference Acceleration
1Houmo AI
Technical Report: Activation Residual Hessian Quantization (ARHQ) for Low-Bit LLM Quantization
Apr 30, 2026YiFeng Wang, Zhun Sun, Keisuke SakaguchiLLM Quantization Activation Quantization
Graduate School of Information Sciences Tohoku University
CoQuant: Joint Weight-Activation Subspace Projection for Mixed-Precision LLMs
Apr 29, 2026Zhe Ding, Su Pan, Duowei PanMixed-Precision Quantization LLM Quantization
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China · Amazon AGI, Seattle, WA, USA
QB-LIF: Learnable-Scale Quantized Burst Neurons for Efficient SNNs
Apr 28, 2026Dewei Bai, Hongxiang Peng, Jiajun Mei +4Efficient Neural Network Inference Spiking Neural Networks
LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
Apr 21, 2026Siqing Song, Chuang Wang, Yong Lang +2LLM Quantization Activation Quantization
MAIS, Institute of Automation, Chinese Academy of Sciences, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, China · Central Media Technology Institute, Huawei
AQPIM: Breaking the PIM Capacity Wall for LLMs with In-Memory Activation Quantization
Apr 20, 2026Kosuke Matsushima, Yasuyuki Okoshi, Masato Motomura +1Compute-in-Memory Long-Context Language Model Inference
Institute of Science Tokyo†
Depth Registers Unlock W4A4 on SwiGLU: A Reader/Generator Decomposition
Apr 20, 2026Ziyang LiuLLM Quantization 4-Bit Quantization
Independent Researcher
DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization
Apr 20, 2026Haokun Lin, Xinle Jia, Haobo Xu +7LLM Quantization 4-Bit Quantization
CASIA · CityU · NJU +3
When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization
Apr 18, 2026Tianqi Li, Wenyu Fang, Xin He +34-Bit Quantization Activation Quantization
VCIP, College of Computer Science, Nankai University · School of Computer Science and Engineering, Tianjin University of Technology · Institute for Infocomm Research, A*STAR +2
DyQ-VLA: Temporal-Dynamic-Aware Quantization for Embodied Vision-Language-Action Models
Mar 9, 2026Zihao Zheng, Hangyu Cao, Chibang Tao +9VLM Quantization Efficient VLA Model Inference
School of Computer Science, Peking University, Beijing, China. · School of Software Engineering, South China University of Technology, Guangzhou, China. · School of Artificial Intelligence, Beijing Normal University, Beijing, China. +1
Activation Quantization of Vision Encoders Needs Prefixing Registers
Oct 6, 2025Seunghyeon Kim, Taesun Yeom, Jinho Kim +3Activation Outliers Activation Quantization
POSTECH · NYU Langone Health2 · Google3
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers
Jun 13, 2025Guang Liang, Xinyao Liu, Jianxin WuQuantization-Aware Training 4-Bit Quantization
State Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China · University of Science and Technology of China, Hefei, China
FPTQuant: Function-Preserving Transforms for LLM Quantization
Jun 5, 2025Boris van Breugel, Yelysei Bondarenko, Paul Whatmough +1LLM Quantization LLM Inference Acceleration
Qualcomm AI Research, Amsterdam, Netherlands

May 3, 2026, cs.ARJ/K move · Enter open · S save

ViM-Q: Scalable Algorithm-Hardware Co-Design for Vision Mamba Model Inference on FPGA

Shengzhe Lyu, Yuhan She, Patrick S. Y. Hung, Ray C. C. Cheung+1

City University of Hong Kong, Hong Kong

Efficient Neural Network Inference Edge Inference Activation Quantization Hardware-Software Co-Design FPGA-Based Neural Network Acceleration Neural Network Quantization

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Vision Mamba (ViM) models offer a compelling efficiency advantage over Transformers by leveraging the linear complexity of State Space Models (SSMs), yet efficiently deploying them on FPGAs remains challenging. Linear layers struggle with dynamic activation outliers that render static quantization ineffective, while uniform quantization fails to capture the weight distribution at low bit-widths. Furthermore, while associative scan accelerates SSMs on GPUs, its memory access patterns are misaligned with the streaming dataflow required by FPGAs. To address these challenges, we present ViM-Q, a scalable algorithm-hardware co-design for end-to-end ViM inference on the edge. We introduce a hardware-aware quantization scheme combining dynamic per-token activation quantization and per-channel smoothing to mitigate outliers, alongside a custom 4-bit per-block Additive Power-of-Two (APoT) weight quantization. The models are deployed on a runtime-parameterizable FPGA accelerator featuring a linear engine employing a Lookup-Table (LUT) unit to replace multiplications with shift-add operations, and a fine-grained pipelined SSM engine that parallelizes the state dimension while preserving sequential recurrence. Crucially, the hardware supports runtime configuration, adapting to diverse dimensions and input resolutions across the ViM family. Implemented on an AMD ZCU102 FPGA, ViM-Q achieves an average 4.96x speedup and 59.8x energy efficiency gain over a quantized NVIDIA RTX 3090 GPU baseline for low-batch inference on ViM-tiny. This co-design shows a viable path for deploying ViM models on resource-constrained edge devices.

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