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 1651–1675 of 4925 papers

TitleStatusHype
Gesture2Text: A Generalizable Decoder for Word-Gesture Keyboards in XR Through Trajectory Coarse Discretization and Pre-training—0
Variable Resolution Pixel Quantization for Low Power Machine Vision Application on Edge—0
Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search—0
Continuous Approximations for Improving Quantization Aware Training of LLMs—0
HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis—0
PalmBench: A Comprehensive Benchmark of Compressed Large Language Models on Mobile Platforms—0
EXAQ: Exponent Aware Quantization For LLMs AccelerationCode0
Generative Semantic Communication for Text-to-Speech Synthesis—0
Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs—0
MIMO Detection with Spatial Sigma-Delta ADCs: A Variational Bayesian Approach—0
SEAL: SEmantic-Augmented Imitation Learning via Language Model—0
Remember and Recall: Associative-Memory-based Trajectory Prediction—0
Overcoming Representation Bias in Fairness-Aware data Repair using Optimal Transport—0
Restorative Speech Enhancement: A Progressive Approach Using SE and Codec Modules—0
Getting Free Bits Back from Rotational Symmetries in LLMs—0
Compressing Recurrent Neural Networks for FPGA-accelerated Implementation in Fluorescence Lifetime Imaging—0
Trainable pruned ternary quantization for medical signal classification modelsCode0
Deep activity propagation via weight initialization in spiking neural networks—0
STanH : Parametric Quantization for Variable Rate Learned Image Compression—0
Aggressive Post-Training Compression on Extremely Large Language Models—0
Constraint Guided Model Quantization of Neural Networks—0
Accelerating PoT Quantization on Edge DevicesCode0
Mixed-Precision Embeddings for Large-Scale Recommendation Models—0
Quantized and Asynchronous Federated Learning—0
Rotated Runtime Smooth: Training-Free Activation Smoother for accurate INT4 inference—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