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 2651–2700 of 4925 papers

TitleStatusHype
Real-Time Distributed Model Predictive Control with Limited Communication Data Rates—0
Efficient Adaptive Activation Rounding for Post-Training Quantization—0
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and PruningCode1
Fast Nearest Convolution for Real-Time Efficient Image Super-ResolutionCode1
Joint Privacy Enhancement and Quantization in Federated LearningCode1
Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform—0
Are disentangled representations all you need to build speaker anonymization systems?—0
Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks—0
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two QuantizationCode1
What Does a One-Bit Quanta Image Sensor Offer?—0
G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model—0
FP8 Quantization: The Power of the ExponentCode1
Towards Practical Single-shot Phase Retrieval with Physics-Driven Deep Neural Network—0
Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis—0
Learning to Structure an Image with Few Colors and Beyond—0
AdaBin: Improving Binary Neural Networks with Adaptive Binary SetsCode0
Design and Analysis of Hardware-limited Non-uniform Task-based Quantizers—0
Adaptive Joint Optimization for 3D Reconstruction with Differentiable Rendering—0
LLM.int8(): 8-bit Matrix Multiplication for Transformers at ScaleCode5
PECAN: A Product-Quantized Content Addressable Memory Network—0
Sampled-data control design for systems with quantized actuators—0
Quantized Adaptive Subgradient Algorithms and Their Applications—0
Mixed-Precision Neural Networks: A Survey—0
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming—0
Learning Quantization in LDPC Decoders—0
Auto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization—0
OL-DN: Online learning based dual-domain network for HEVC intra frame quality enhancement—0
Design of High-Throughput Mixed-Precision CNN Accelerators on FPGA—0
Quantifying the Capacity Gains in Coarsely Quantized SISO Systems with Nonlinear Analog Operators—0
Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems with Low-Resolution Data Converters—0
Towards Semantic Communications: Deep Learning-Based Image Semantic Coding—0
Human Perception as a Phenomenon of Quantization—0
Quantization enabled Privacy Protection in Decentralized Stochastic Optimization—0
Study of Encoder-Decoder Architectures for Code-Mix Search Query Translation—0
DIVISION: Memory Efficient Training via Dual Activation PrecisionCode0
FBI: Fingerprinting models with Benign Inputs—0
QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM—0
Privacy-Preserving Action Recognition via Motion Difference QuantizationCode1
Keyword Spotting System and Evaluation of Pruning and Quantization Methods on Low-power Edge MicrocontrollersCode1
PalQuant: Accelerating High-precision Networks on Low-precision AcceleratorsCode1
The Effect of Points Dispersion on the k-nn Search in Random Projection ForestsCode0
Multi-user Downlink Beamforming using Uplink Downlink Duality with CEQs for Frequency Selective Channels—0
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks—0
Ultra-low Latency Adaptive Local Binary Spiking Neural Network with Accuracy Loss Estimator—0
Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers—0
Symmetry Regularization and Saturating Nonlinearity for Robust Quantization—0
enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural NetworksCode0
Distilled Low Rank Neural Radiance Field with Quantization for Light Field Compression—0
BiFeat: Supercharge GNN Training via Graph Feature QuantizationCode0
Evaluating the Practicality of Learned Image Compression—0
Show:102550
← PrevPage 54 of 99Next →
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