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 1276–1300 of 4925 papers

TitleStatusHype
EmbBERT-Q: Breaking Memory Barriers in Embedded NLPCode0
Towards Watermarking of Open-Source LLMs—0
Low-Complexity On-Grid Channel Estimation for Partially-Connected Hybrid XL-MIMO—0
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models—0
NestQuant: Nested Lattice Quantization for Matrix Products and LLMs—0
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits—0
Contextual Compression Encoding for Large Language Models: A Novel Framework for Multi-Layered Parameter Space Pruning—0
Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding—0
Loss Landscape Analysis for Reliable Quantized ML Models for Scientific SensingCode0
Scalable Thermodynamic Second-order Optimization—0
Exploiting Non-uniform Quantization for Enhanced ILC in Wideband Digital Pre-distortion—0
Conditional Distribution Quantization in Machine Learning—0
Column-wise Quantization of Weights and Partial Sums for Accurate and Efficient Compute-In-Memory AcceleratorsCode0
Vision-Language Models for Edge Networks: A Comprehensive Survey—0
HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates—0
MEMHD: Memory-Efficient Multi-Centroid Hyperdimensional Computing for Fully-Utilized In-Memory Computing Architectures—0
GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing UnitsCode0
Matryoshka Quantization—0
Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation—0
Demystifying Singular Defects in Large Language Models—0
Gradient Based Method for the Fusion of Lattice Quantizers—0
Physics-Conditioned Diffusion Models for Lattice Gauge TheoryCode0
Scalable and consistent embedding of probability measures into Hilbert spaces via measure quantization—0
Efficient Evaluation of Quantization-Effects in Neural Codecs—0
QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation—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