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 2201–2225 of 4925 papers

TitleStatusHype
Stability Analysis of Various Symbolic Rule Extraction Methods from Recurrent Neural Network—0
Leveraging Continuously Differentiable Activation Functions for Learning in Quantized Noisy EnvironmentsCode0
Locally-Adaptive Quantization for Streaming Vector Search—0
Ultrafast jet classification on FPGAs for the HL-LHCCode0
Truncated Non-Uniform Quantization for Distributed SGD—0
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation—0
Faster Inference of Integer SWIN Transformer by Removing the GELU Activation—0
An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec—0
Improved Quantization Strategies for Managing Heavy-tailed Gradients in Distributed Learning—0
Neural Language of Thought Models—0
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign DecodingCode0
HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays—0
Can Large Language Models Understand Context?—0
Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach—0
Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs—0
One-Step Forward and Backtrack: Overcoming Zig-Zagging in Loss-Aware Quantization TrainingCode0
Effect of Weight Quantization on Learning Models by Typical Case Analysis—0
Effective Communication with Dynamic Feature CompressionCode0
HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference—0
Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval—0
A Comprehensive Survey of Compression Algorithms for Language Models—0
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
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks—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