Neural Network Quantization

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36 papers in the last four weeks, up 125% on the four weeks before. 0.4% of all new papers.

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Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection
Jul 22, 2026Shrinidhi Sridhar, Vikas K. MalviyaEnergy-Efficient ML Android Malware Detection
MIE-SPPU Institute of Higher Education · Doha, Qatar
Contraction-Gauge Preconditioning for Quantized Matrix Multiplication
Jul 21, 2026Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara +2Post-Training Quantization Low-Bit Quantization
Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
Recti-Q: Feature-Space Rectification for Out-of-Distribution-Robust Quantized Perception in Edge Robotics
Jul 20, 2026Hamidreza Yaghoubi Araghi, Parastoo Pilevar, Ming C. LinDistribution Shift Robustness Image Corruption Robustness
University of Maryland, College Park, MD, USA
MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference
Jul 20, 2026Simla Burcu Harma, Danila Mishin, Zhengyuan Su +7Mixed-Precision Quantization LLM Quantization
EcoCloud, EPFL · Tsinghua University · MangoBoost Inc. +2
Toward Energy-Efficient and Low-Power Arrhythmia Detection for Wearable Devices
Jul 16, 2026Floriaan Bulten, Yawar Rasheed, Arlene John +2Energy-Efficient ML Electrocardiogram Classification
Computer Architecture for Embedded Systems, University of Twente, The Netherlands · Biomedical Signals and Systems, University of Twente, The Netherlands · Faculty of Science, Computer Science, Vrije Universiteit Amsterdam, The Netherlands
Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models
Jul 16, 2026Ryona NodaLLM Quantization Post-Training Quantization
Ryo Noda
PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference
Jul 16, 2026Hyunwoo Oh, Suyeon Jang, Hanning Chen +4Mixed-Precision Quantization LLM Quantization
University of California, Irvine
ExTernD: Expanded-Rank Ternary Decomposition Ternary LLM PTQ with Accuracy Approaching Any Quantization Level
Jul 15, 2026Chethan Reddy G. PLLM Quantization Ternary Quantization
Saturation Makes Quantization Error Additive: A Coverage Model with a Certificate
Jul 14, 2026Joshua HillMixed-Precision Quantization LLM Quantization
Baseten Labs, Inc. · David R. Cheriton School of Computer Science, University of Waterloo
Efficient and Robust Spiking Neural Networks for sEMG-Based Muscle Fatigue Detection
Jul 13, 2026Kaiwen Tang, Jiaqi Dong, Zhanglu Yan +1Energy-Efficient ML Spiking Neural Networks
University of Singapore
The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
Jul 9, 2026Baha Rababah, Cuneyt Gurcan Akcora, Carson K. LeungLLM Evaluation Post-Training Quantization
Department of Computer Science, University of Manitoba, Canada · AI Initiative, University of Central Florida, USA
It Takes Few to TANGO: A Quantized Distributed Model for Binaural Speech Enhancement
Jul 9, 2026Zahra Benslimane, Pierre Chouteau, Martyna Poreba +4Quantization-Aware Training Speech Enhancement
Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France · Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France
Closing the Null Space: Guidance-Aware Quantization for Classifier-Free Diffusion
Jul 9, 2026Abdullah Al Shafi, Sumaiya Rahim SumaMixed-Precision Quantization Classifier-Free Guidance
Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna-9203, Bangladesh
Rethinking Small VLM Quantization: From Component-Wise Analysis to Hardware-Aware Edge Deployment
Jul 9, 2026Hyeju Shin, Chorwon Kim, Ryangsoo Kim +2Mixed-Precision Quantization Vision-Language Models
1ETRI, Republic of Korea.
KronQ: LLM Quantization via Kronecker-Factored Hessian
Jul 8, 2026Donghyun Lee, Yuhang Li, Ruokai Yin +1Mixed-Precision Quantization LLM Quantization
University of Southern California · Yale University
Lyapunov-Guided Training for Hardware-Safe Neural Networks Under Fixed-Point Arithmetic
Jul 5, 2026Anis Hamadouche, Amir HussainLyapunov-Based Control Quantization-Aware Training
The School of Engineering & Physical Sciences Heriot-Watt University Edinburgh EH14 4AS, UK · SDAIA-KFUPM Joint Research Centre for Artificial Intelligence King Fahd University of Petroleum and Minerals Dhahran, Saudi Arabia
Full-Stack FP4: Stable LLM Pretraining with Quantized Projections, Optimizers, and Attention
Jul 5, 2026Siyu Ding, Mingchuan Ma, Jiabo Tong +3Language Model Pretraining Mixed-Precision Quantization
Institute of Automation, Chinese Academy of Sciences · Sichuan University · Beijing Zhongguancun Academy +3
HiFA4: Training-Free 4-bit FlashAttention on Ascend HIF4 NPUs for LLM Inference
Jul 5, 2026Hui Dong, Yanzhao Li, Jie Gao +5Softmax Attention LLM Quantization
Huawei Technologies
A Unified Framework for Quantized and Continuous Strong Lottery Tickets
Jul 4, 2026Aakash Kumar, Emanuele NataleNeural Network Approximation Theory Neural Network Quantization
COATI, CNRS, Inria, I3S, Université Coté d’Azur, France
ELiTeFormer: An Efficient Transformer for FPGAs
Jul 4, 2026Victor Agostinelli, Nicolas Bohm Agostini, Antonino TumeoLLM Quantization Ternary Quantization
Oregon State University Corvallis, Oregon, USA · Pacific Northwest National Laboratory Richland, Washington, USA
Variable Bit-width Quantization: Learning Per-Group Precision for "Bigger-but-Smaller" Language Models
Jul 3, 2026Hamish OgilvyMixed-Precision Quantization LLM Quantization
Algolia Research
OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers
Jul 2, 2026Donghyun Lee, Jitesh Chavan, Duy Nguyen +5Video Diffusion Models Diffusion Transformer
Cantina Labs · University of Southern California · *Work done during an internship at Cantina Labs. +1
Boundary-Aware Quantization: Finite-Scale Decision Geometry of Neural Classifiers
Jul 1, 2026O. M. KiselevWeight-Only Quantization Post-Training Quantization
Innopolis University, Innopolis, Republic of Tatarstan, Russian Federation
$\text{Log}_\text{b}$ Quant: Quantizing Language Models in Logarithmic Space
Jul 1, 2026Jeremias Bohn, Tizian Dippold, Mahdi Koubaa +2LLM Quantization 4-Bit Quantization
School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
Beyond Activation Alignment:The Alignment-Diversity Tradeoff in Task-Aware LLM Quantization
Jul 1, 2026Fei Wang, Chao Xue, Taoran Liu +3Mixed-Precision Quantization LLM Quantization
South China University of Technology · Ocean University of China · Shenzhen Campus of Sun Yat-sen University +1
Zero-Shot Quantization for Object Detectors using Off-the-Shelf Generative Models
Jun 30, 2026Hyunho Lee, Kyomin Hwang, Hyeonjin Kim +3Quantization-Aware Training Synthetic Data Generation
Seoul National University, Seoul, South Korea · LG Electronics, Seoul, South Korea
W4A4 Quantization for Inference on Wan2.2-I2V-A14B
Jun 28, 2026Yidong Chen, Chengyu Shi, Jiahao LiuDiffusion Model Quantization Post-Training Quantization
Tsinghua University
SEADA: An efficient methodology for optimizing mixed-precision DNNs on multi-precision spatial architectures
Jun 26, 2026Leandro Fiorin, Marco Ronzani, Cristina SilvanoMixed-Precision Quantization Efficient Neural Network Inference
Indipendent Researcher Groningen, The Netherlands · Dipartimento di Elettronica e Informazione, Politecnico di Milano. Milano, Italy
Learning 1-Bit LiDAR-based Localization with Auxiliary Objective
Jun 26, 2026Kaijie Yin, Zhiyuan Zhang, Tian Gao +3Binary Neural Networks LiDAR Localization
University of Macau · Singapore Management University · Eastern Institute of Technology, Ningbo
Quantization in Federated Learning: Methods, Challenges and Future Directions
Jun 25, 2026Farwa Ikram, Dipanwita Thakur, Antonella Guzzo +1Neural Network Quantization Federated Learning
DIMES - University of Calabria, Italy

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Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection

Shrinidhi Sridhar, Vikas K. Malviya

MIE-SPPU Institute of Higher Education · Doha, Qatar

Energy-Efficient ML Android Malware Detection Neural Network Quantization On-Device Inference

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An increase in advanced Android malware requires the use of deep learning models, which can run on Android devices. But there is a trade-off between security and energy use, as strong detection models can drain the battery of devices fast. This work tests different Multi-Layer Perceptron (MLP) model configurations to balance malware detection performance and energy efficiency. In this work, we compared standard FP32 models with optimized INT8 quantized neural networks with different model depths using TUANDROMD and DREBIN datasets for both classification performance and energy consumption. The results show that INT8 quantization reduces model size by about 3.5 times with a decrease in energy consumption to 0.0189 mJ per inference, while maintaining more than 99.2% detection accuracy. We found that shallow quantized architectures, such as 3-layer and 4-layer QNNs, reduce energy costs by improving throughput and shortening the time of CPU operating in a high-power state. This work shows that efficient malware protection can be achieved on resource-constrained smartphones and provides a foundation for Green AI in mobile security.

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Latest papers 270