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 3626–3650 of 4925 papers

TitleStatusHype
Nonlinear Sparse Bayesian Learning Methods with Application to Massive MIMO Channel Estimation with Hardware Impairments—0
Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees—0
Nonparametric Decentralized Detection and Sparse Sensor Selection via Multi-Sensor Online Kernel Scalar Quantization—0
Non-Structured DNN Weight Pruning -- Is It Beneficial in Any Platform?—0
Non-vacuous Generalization Bounds for Deep Neural Networks without any modification to the trained models—0
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks—0
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models—0
No Token Left Behind: Reliable KV Cache Compression via Importance-Aware Mixed Precision Quantization—0
Novel Near-Optimal Scalar Quantizers with Exponential Decay Rate and Global Convergence—0
NQKV: A KV Cache Quantization Scheme Based on Normal Distribution Characteristics—0
NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache—0
NTP : A Neural Network Topology Profiler—0
NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search—0
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization—0
NVRC: Neural Video Representation Compression—0
O(1) Communication for Distributed SGD through Two-Level Gradient Averaging—0
OAC: Output-adaptive Calibration for Accurate Post-training Quantization—0
Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization—0
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes—0
Object Detection-Based Variable Quantization Processing—0
Object Proposal Generation using Two-Stage Cascade SVMs—0
ODG-Q: Robust Quantization via Online Domain Generalization—0
Tighter Regret Analysis and Optimization of Online Federated Learning—0
On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks—0
Oh! We Freeze: Improving Quantized Knowledge Distillation via Signal Propagation Analysis for Large Language Models—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