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 1451–1475 of 4925 papers

TitleStatusHype
Privacy-Preserving UCB Decision Process Verification via zk-SNARKs—0
LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory—0
Variational quantization for state space modelsCode0
Neural Network Approach for Non-Markovian Dissipative Dynamics of Many-Body Open Quantum Systems—0
QGen: On the Ability to Generalize in Quantization Aware Training—0
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
Tripod: Three Complementary Inductive Biases for Disentangled Representation LearningCode1
SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural NetworksCode5
Quantization of Large Language Models with an Overdetermined Basis—0
Efficient and accurate neural field reconstruction using resistive memory—0
TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models—0
SNN4Agents: A Framework for Developing Energy-Efficient Embodied Spiking Neural Networks for Autonomous AgentsCode0
Bullion: A Column Store for Machine Learning—0
Full-Duplex Beyond Self-Interference: The Unlimited Sensing Way—0
Lossy Image Compression with Foundation Diffusion Models—0
Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysis—0
1-bit Quantized On-chip Hybrid Diffraction Neural Network Enabled by Authentic All-optical Fully-connected Architecture—0
Frame Quantization of Neural Networks—0
Differentiable Search for Finding Optimal Quantization Strategy—0
CQIL: Inference Latency Optimization with Concurrent Computation of Quasi-Independent LayersCode0
Adapting LLaMA Decoder to Vision TransformerCode1
End-to-End Rate-Distortion Optimized 3D Gaussian RepresentationCode1
Encoder-Quantization-Motion-based Video Quality Metrics—0
AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information RetrievalCode2
Collaborative Edge AI Inference over Cloud-RAN—0
Show:102550
← PrevPage 59 of 197Next →
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