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 4551–4575 of 4925 papers

TitleStatusHype
Deep Metric Learning to RankCode0
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone MicrocontrollerCode0
Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-LevelsCode0
AdaBin: Improving Binary Neural Networks with Adaptive Binary SetsCode0
Empirical Evaluation of Deep Learning Model Compression Techniques on the WaveNet VocoderCode0
PLUM: Improving Inference Efficiency By Leveraging Repetition-Sparsity Trade-OffCode0
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign DecodingCode0
Open-source FPGA-ML codesign for the MLPerf Tiny BenchmarkCode0
Learning Space Partitions for Nearest Neighbor SearchCode0
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN AcceleratorsCode0
Operations Guided Neural Networks for High Fidelity Data-To-Text GenerationCode0
SignSGD with Federated VotingCode0
Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph RepresentationCode0
EmbBERT-Q: Breaking Memory Barriers in Embedded NLPCode0
Randomized Quantization is All You Need for Differential Privacy in Federated LearningCode0
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based ApproachCode0
Elastic Product Quantization for Time SeriesCode0
Learning Semantic Textual Similarity via Topic-informed Discrete Latent VariablesCode0
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware TrainingCode0
Task-Based Graph Signal CompressionCode0
Unconditional Image-Text Pair Generation with Multimodal Cross QuantizerCode0
Deep Log-Likelihood Ratio QuantizationCode0
Learning Physical-Layer Communication with Quantized FeedbackCode0
Deep Learning with Low Precision by Half-wave Gaussian QuantizationCode0
Efficiera Residual Networks: Hardware-Friendly Fully Binary Weight with 2-bit Activation Model Achieves Practical ImageNet AccuracyCode0
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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