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 2876–2900 of 4925 papers

TitleStatusHype
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed ClusterCode5
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data—0
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense EmbeddingsCode1
Scaling Language Model Size in Cross-Device Federated Learning—0
It's All In the Teacher: Zero-Shot Quantization Brought Closer to the TeacherCode1
Ternary and Binary Quantization for Improved Classification—0
Autoregressive Co-Training for Learning Discrete Speech RepresentationsCode0
4-bit Conformer with Native Quantization Aware Training for Speech RecognitionCode2
Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking—0
Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform—0
Reverse Link Analysis for Full-Duplex Cellular Networks with Low Resolution ADC/DAC—0
New pyramidal hybrid textural and deep features based automatic skin cancer classification model: Ensemble DarkNet and textural feature extractor—0
REx: Data-Free Residual Quantization Error Expansion—0
SPIQ: Data-Free Per-Channel Static Input Quantization—0
Pseudo-Label Transfer from Frame-Level to Note-Level in a Teacher-Student Framework for Singing Transcription from Polyphonic MusicCode1
LAMBDA: Covering the Solution Set of Black-Box Inequality by Search Space Quantization—0
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning SimulationsCode1
MKQ-BERT: Quantized BERT with 4-bits Weights and Activations—0
Efficient-VDVAE: Less is moreCode1
Mokey: Enabling Narrow Fixed-Point Inference for Out-of-the-Box Floating-Point Transformer Models—0
Fast on-line signature recognition based on VQ with time modeling—0
FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation—0
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and QuantizationCode1
Overcoming Oscillations in Quantization-Aware TrainingCode1
Compression of Generative Pre-trained Language Models via Quantization—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