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 4751–4775 of 4925 papers

TitleStatusHype
Grouped Sequency-arranged Rotation: Optimizing Rotation Transformation for Quantization for Free—0
Group Invariant Deep Representations for Image Instance Retrieval—0
Group Quantization of Quadratic Hamiltonians in Finance—0
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking—0
Group Sparse Coding—0
GSVR: 2D Gaussian-based Video Representation for 800+ FPS with Hybrid Deformation Field—0
Guaranteed Quantization Error Computation for Neural Network Model Compression—0
Gull: A Generative Multifunctional Audio Codec—0
GWQ: Gradient-Aware Weight Quantization for Large Language Models—0
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks—0
HACK: Homomorphic Acceleration via Compression of the Key-Value Cache for Disaggregated LLM Inference—0
Hadamard Domain Training with Integers for Class Incremental Quantized Learning—0
HadaNets: Flexible Quantization Strategies for Neural Networks—0
HadaNorm: Diffusion Transformer Quantization through Mean-Centered Transformations—0
HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis—0
HALO: Hardware-aware quantization with low critical-path-delay weights for LLM acceleration—0
LANA: Latency Aware Network Acceleration—0
HAO: Hardware-aware neural Architecture Optimization for Efficient Inference—0
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights—0
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization—0
Hardware-Centric AutoML for Mixed-Precision Quantization—0
Hardware-friendly Deep Learning by Network Quantization and Binarization—0
Hardware-Friendly Static Quantization Method for Video Diffusion Transformers—0
Hardware Implementation of Task-based Quantization in Multi-user Signal Recovery—0
Hardware Limitations and Optimization Approach in 1-Bit RIS Design at 28 GHz—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