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 1426–1450 of 4925 papers

TitleStatusHype
Effect of Weight Quantization on Learning Models by Typical Case Analysis—0
Effects of VLSI Circuit Constraints on Temporal-Coding Multilayer Spiking Neural Networks—0
Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion—0
Efficient Adaptive Activation Rounding for Post-Training Quantization—0
Efficient-Adam: Communication-Efficient Distributed Adam—0
Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications—0
Efficient and accurate neural field reconstruction using resistive memory—0
Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference—0
Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines—0
Enhancing Diversity for Data-free Quantization—0
Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing—0
Efficient ANN-SNN Conversion with Error Compensation Learning—0
Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores—0
Enhancing Field-Oriented Control of Electric Drives with Tiny Neural Network Optimized for Micro-controllers—0
Enhancing Off-Grid One-Bit DOA Estimation with Learning-Based Sparse Bayesian Approach for Non-Uniform Sparse Array—0
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost—0
Efficient Bitwidth Search for Practical Mixed Precision Neural Network—0
Efficient Channel Estimator with Angle-Division Multiple Access—0
Bridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression—0
Efficient Compression of Multitask Multilingual Speech Models—0
Bridging the Modality Gap: Softly Discretizing Audio Representation for LLM-based Automatic Speech Recognition—0
EntroLLM: Entropy Encoded Weight Compression for Efficient Large Language Model Inference on Edge Devices—0
Efficient Convolutional Neural Network with Binary Quantization Layer—0
BRIEDGE: EEG-Adaptive Edge AI for Multi-Brain to Multi-Robot Interaction—0
ERQ: Error Reduction for Post-Training Quantization of Vision Transformers—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