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 1501–1525 of 4925 papers

TitleStatusHype
Reducing Inference Energy Consumption Using Dual Complementary CNNsCode0
RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for Boosting 2-bit Large Language Model AccuracyCode0
Quantization-Aware Imitation-Learning for Resource-Efficient Robotic Control—0
Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features—0
Memory-Efficient Training for Deep Speaker Embedding Learning in Speaker Verification—0
A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series—0
LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization—0
CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation—0
Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation—0
Quantized Delta Weight Is Safety Keeper—0
DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding—0
On the effectiveness of discrete representations in sparse mixture of experts—0
Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads—0
FAMES: Fast Approximate Multiplier Substitution for Mixed-Precision Quantized DNNs--Down to 2 Bits!—0
SoftmAP: Software-Hardware Co-design for Integer-Only Softmax on Associative Processors—0
Rapid Deployment of Domain-specific Hyperspectral Image Processors with Application to Autonomous Driving—0
COAP: Memory-Efficient Training with Correlation-Aware Gradient Projection—0
Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens—0
LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and QuantizationCode0
MixPE: Quantization and Hardware Co-design for Efficient LLM Inference—0
Lion Cub: Minimizing Communication Overhead in Distributed Lion—0
Factorized Visual Tokenization and Generation—0
Representation Collapsing Problems in Vector Quantization—0
Downlink MIMO Channel Estimation from Bits: Recoverability and Algorithm—0
SKQVC: One-Shot Voice Conversion by K-Means Quantization with Self-Supervised Speech Representations—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