Quantized Neural Network

Quantized neural networks (QNNs) aim to reduce the computational cost and memory footprint of deep learning models by representing weights and activations using lower-precision integer arithmetic, rather than 32-bit floating-point numbers. Current research focuses on improving the accuracy of QNNs through techniques like quantization-aware training, exploring different quantization schemes (e.g., mixed-precision, stochastic quantization), and developing efficient algorithms for training and verification. This field is significant because QNNs enable the deployment of deep learning on resource-constrained devices, impacting applications ranging from mobile and edge computing to embedded systems and Internet of Things (IoT) devices.

Papers

October 7, 2024

MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signals
Ajay B S, Phani Pavan K, Madhav Rao
Long Short Term Memory Physiological Signal Stress Detection Quantized Neural Network Neuron Activation Compartmental Neuron Evolutionary Deep Learning

August 30, 2024

On Expressive Power of Quantized Neural Networks under Fixed-Point Arithmetic
Geonho Hwang, Yeachan Park, Sejun Park
Universal Approximation Expressive Power Fixed Point Quantized Neural Network

April 9, 2024

AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information Retrieval
Kento Tatsuno, Daisuke Miyashita, Taiga Ikeda, Kiyoshi Ishiyama, Kazunari Sumiyoshi, Jun Deguchi
Information Retrieval Approximate Nearest Neighbor Search Quantized Neural Network Product Quantization Graph Based Approximate Nearest Neighbor

March 22, 2024

Magic for the Age of Quantized DNNs
Yoshihide Sawada, Ryuji Saiin, Kazuma Suetake
Speech Based Age Quantization Aware Training LangId Magic Spell Batch Normalization Layer Quantized Neural Network Product Quantization New Normalization Technique Quantised Neural Network

December 20, 2023

Towards Efficient Verification of Quantized Neural Networks
Pei Huang, Haoze Wu, Yuting Yang, Ieva Daukantas, Min Wu, Yedi Zhang, Clark Barrett
Quantization Operator Device Inference Quantized Neural Network Efficient Verification Bound Propagation Perception Network

December 14, 2023

Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition
Oliver Grainge, Michael Milford, Indu Bodala, Sarvapali D. Ramchurn, Shoaib Ehsan
Visual Place Recognition Perception System Design Space Exploration Quantized Neural Network Global Re Localization Recall@K Metric High Recall

November 30, 2023

Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training
Saurabh Farkya, Aswin Raghavan, Avi Ziskind
Adversarial Attack Native Robustness Adversarial Training Ensemble Learning White Box Quantized Neural Network Different Quantization Stochastic Quantization

November 6, 2023

Training Multi-layer Neural Networks on Ising Machine
Xujie Song, Tong Liu, Shengbo Eben Li, Jingliang Duan, Wenxuan Wang, Keqiang Li
Quadratic Unconstrained Binary Quantized Neural Network Gradient Backpropagation Ising Machine Multi Layer Neural Network

October 29, 2023

QWID: Quantized Weed Identification Deep neural network
Parikshit Singh Rathore
Neural Network Multiplier Free Quantization Quantized Neural Network InceptionV3 Model Weed Detection Weed Datasets

September 25, 2023

On Calibration of Modern Quantized Efficient Neural Networks
Joey Kuang, Alexander Wong
Calibration Performance Calibration Error Quantized Neural Network Quantization Performance

July 29, 2023

An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks
Ye Tao, Wanwei Liu, Fu Song, Zhen Liang, Ji Wang, Hongxu Zhu
Neural Network Deep Neural Network DNN Framework Multiplier Free Quantization Quantized Neural Network Automaton Theoretic Approach

July 8, 2023

Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives
Olga Krestinskaya, Li Zhang, Khaled Nabil Salama
State of the Art Synthesized View Open Challenge Memory Computing Computing in Memory Quantized Neural Network

June 23, 2023

QNNRepair: Quantized Neural Network Repair
Xidan Song, Youcheng Sun, Mustafa A. Mustafa, Lucas C. Cordeiro
Quantized Neural Network DNN Repair Neural Network Quantization

June 16, 2023

Enhancing Fault Resilience of QNNs by Selective Neuron Splitting
Mohammad Hasan Ahmadilivani, Mahdi Taheri, Jaan Raik, Masoud Daneshtalab, Maksim Jenihhin
Neural Network DNN Accelerator Quantized Neural Network Critical Neuron Fault Resilience

May 25, 2023

PQA: Exploring the Potential of Product Quantization in DNN Hardware Acceleration
Ahmed F. AbouElhamayed, Angela Cui, Javier Fernandez-Marques, Nicholas D. Lane, Mohamed S. Abdelfattah
Full Potential DNN Accelerator Quantized Neural Network Product Quantization

March 15, 2023

SpaceEvo: Hardware-Friendly Search Space Design for Efficient INT8 Inference
Li Lyna Zhang, Xudong Wang, Jiahang Xu, Quanlu Zhang, Yujing Wang, Yuqing Yang, Ningxin Zheng, Ting Cao, Mao Yang
Neural Architecture Search Quantization Technique Quantized Neural Network Quantization Search

December 29, 2022

FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks
Akul Malhotra, Sumeet Kumar Gupta
Activation Sparsity Quantized Neural Network Fault Tolerance Document Flattening

December 10, 2022

November 29, 2022

Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks
Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger, Daniela Rus
Neural Network Adversarial Attack Adversarial Robustness Quantized Neural Network Quantization Level Interval Bound Propagation