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 2676–2700 of 4925 papers

TitleStatusHype
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
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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