SOTAVerified

Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1851–1900 of 4925 papers

TitleStatusHype
A Different View of Sigma-Delta Modulators Under the Lens of Pulse Frequency Modulation—0
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own—0
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation—0
FoVolNet: Fast Volume Rendering using Foveated Deep Neural Networks—0
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models—0
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization—0
Comparison of 14 different families of classification algorithms on 115 binary datasets—0
Feature Quantization for Defending Against Distortion of Images—0
FP8-BERT: Post-Training Quantization for Transformer—0
Comparing Iterative and Least-Squares Based Phase Noise Tracking in Receivers with 1-bit Quantization and Oversampling—0
High-performance deep spiking neural networks with 0.3 spikes per neuron—0
FP8 versus INT8 for efficient deep learning inference—0
Comparing Fisher Information Regularization with Distillation for DNN Quantization—0
FPGA Resource-aware Structured Pruning for Real-Time Neural Networks—0
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data—0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion—0
FPTQ: Fine-grained Post-Training Quantization for Large Language Models—0
FPTQuant: Function-Preserving Transforms for LLM Quantization—0
FP=xINT:A Low-Bit Series Expansion Algorithm for Post-Training Quantization—0
ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers—0
FD-LSCIC: Frequency Decomposition-based Learned Screen Content Image Compression—0
FDD Massive MIMO: How to Optimally Combine UL Pilot and Limited DL CSI Feedback?—0
FD Cell-Free mMIMO: Analysis and Optimization—0
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
FCN-Pose: A Pruned and Quantized CNN for Robot Pose Estimation for Constrained Devices—0
Frame Quantization of Neural Networks—0
Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge—0
FBQuant: FeedBack Quantization for Large Language Models—0
Frequency Autoregressive Image Generation with Continuous Tokens—0
Frequency-Biased Synergistic Design for Image Compression and Compensation—0
Frequency Disentangled Features in Neural Image Compression—0
Compact Representation for Image Classification: To Choose or to Compress?—0
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks—0
FBI: Fingerprinting models with Benign Inputs—0
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms—0
Are disentangled representations all you need to build speaker anonymization systems?—0
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
From Text to Source: Results in Detecting Large Language Model-Generated Content—0
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization—0
Fronthaul Compression and Passive Beamforming Design for Intelligent Reflecting Surface-aided Cloud Radio Access Networks—0
Fronthaul-Constrained Distributed Radar Sensing—0
Fronthaul Quantization-Aware MU-MIMO Precoding for Sum Rate Maximization—0
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary—0
Accelerator-Aware Training for Transducer-Based Speech Recognition—0
FTL: A universal framework for training low-bit DNNs via Feature Transfer—0
Fault-Tolerant Four-Dimensional Constellation for Coherent Optical Transmission Systems—0
Compact Neural Graphics Primitives with Learned Hash Probing—0
FATNN: Fast and Accurate Ternary Neural Networks—0
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks—0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified