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
Nanoscaling Floating-Point (NxFP): NanoMantissa, Adaptive Microexponents, and Code Recycling for Direct-Cast Compression of Large Language Models—0
TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs—0
TinySubNets: An efficient and low capacity continual learning strategyCode0
Enhancing Off-Grid One-Bit DOA Estimation with Learning-Based Sparse Bayesian Approach for Non-Uniform Sparse Array—0
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks—0
Memory-Efficient 4-bit Preconditioned Stochastic Optimization—0
Progressive Compression with Universally Quantized Diffusion Models—0
Efficient Generative Modeling with Residual Vector Quantization-Based Tokens—0
Panacea: Novel DNN Accelerator using Accuracy-Preserving Asymmetric Quantization and Energy-Saving Bit-Slice Sparsity—0
TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation—0
VQTalker: Towards Multilingual Talking Avatars through Facial Motion Tokenization—0
MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization—0
DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations—0
Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices—0
On Round-Off Errors and Gaussian Blur in Superresolution and in Image Registration—0
CRVQ: Channel-relaxed Vector Quantization for Extreme Compression of LLMs—0
TurboAttention: Efficient Attention Approximation For High Throughputs LLMs—0
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection—0
Post-Training Non-Uniform Quantization for Convolutional Neural Networks—0
QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex—0
Low-Rank Correction for Quantized LLMs—0
Machine learning-driven conservative-to-primitive conversion in hybrid piecewise polytropic and tabulated equations of state—0
Compression for Better: A General and Stable Lossless Compression Framework—0
Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion—0
Federated Split Learning with Model Pruning and Gradient Quantization in Wireless 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