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 1676–1700 of 4925 papers

TitleStatusHype
Can Large Language Models Understand Context?—0
Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach—0
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache QuantizationCode3
Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs—0
Effect of Weight Quantization on Learning Models by Typical Case Analysis—0
One-Step Forward and Backtrack: Overcoming Zig-Zagging in Loss-Aware Quantization TrainingCode0
HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference—0
Effective Communication with Dynamic Feature CompressionCode0
Scaling Sparse Fine-Tuning to Large Language ModelsCode1
LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object DetectionCode2
Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval—0
A Comprehensive Survey of Compression Algorithms for Language Models—0
Residual Quantization with Implicit Neural CodebooksCode2
MPTQ-ViT: Mixed-Precision Post-Training Quantization for Vision Transformer—0
LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization—0
Within-basket Recommendation via Neural Pattern Associator—0
FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-DesignCode3
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks—0
Towards Cheaper Inference in Deep Networks with Lower Bit-Width Accumulators—0
Value-Driven Mixed-Precision Quantization for Patch-Based Inference on Microcontrollers—0
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces—0
Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge—0
Robustness to distribution shifts of compressed networks for edge devices—0
Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding—0
Edge-Enabled Real-time Railway Track Segmentation—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