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

Showing 18011825 of 4925 papers

TitleStatusHype
Fault-Tolerant Four-Dimensional Constellation for Coherent Optical Transmission Systems0
BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality AssessmentCode1
LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPSCode2
Fast and Efficient 2-bit LLM Inference on GPU: 2/4/16-bit in a Weight Matrix with Asynchronous Dequantization0
TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion ModelsCode1
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence0
PIPE : Parallelized Inference Through Post-Training Quantization Ensembling of Residual Expansions0
CUCL: Codebook for Unsupervised Continual LearningCode0
SNN Architecture for Differential Time Encoding Using Decoupled Processing Time0
BinaryHPE: 3D Human Pose and Shape Estimation via BinarizationCode1
SySMOL: Co-designing Algorithms and Hardware for Neural Networks with Heterogeneous Precisions0
A Blockchain Solution for Collaborative Machine Learning over IoT0
Modulation For Modulo: A Sampling-Efficient High-Dynamic Range ADC0
Compact 3D Gaussian Representation for Radiance FieldCode2
Uncertainty Estimation in Multi-Agent Distributed Learning0
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and QuantizationCode1
Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming0
Eliminating Quantization Errors in Classification-Based Sound Source LocalizationCode0
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs0
McQueen : Mixed Precision Quantization of Early Exit NetworksCode0
Tiny-VBF: Resource-Efficient Vision Transformer based Lightweight Beamformer for Ultrasound Single-Angle Plane Wave Imaging0
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review0
LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model FinetuningCode1
Low-Precision Floating-Point for Efficient On-Board Deep Neural Network Processing0
Compressed 3D Gaussian Splatting for Accelerated Novel View SynthesisCode0
Show:102550
← PrevPage 73 of 197Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17Unverified
8Xception W8A8Top-1 Accuracy (%)78.97Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04Unverified
2DTQMAP0.79Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3Unverified
2OutEffHop-Bert_basePerplexity6.21Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8Unverified