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 4776–4800 of 4925 papers

TitleStatusHype
HarmLevelBench: Evaluating Harm-Level Compliance and the Impact of Quantization on Model Alignment—0
Harnessing Unrecognizable Faces for Improving Face Recognition—0
Harnessing Your DRAM and SSD for Sustainable and Accessible LLM Inference with Mixed-Precision and Multi-level Caching—0
HARP-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding—0
Hashing in the Zero Shot Framework with Domain Adaptation—0
Hashing on Nonlinear Manifolds—0
NeuroNAS: Enhancing Efficiency of Neuromorphic In-Memory Computing for Intelligent Mobile Agents through Hardware-Aware Spiking Neural Architecture Search—0
HCE: Improving Performance and Efficiency with Heterogeneously Compressed Neural Network Ensemble—0
HCLAE: High Capacity Locally Aggregating Encodings for Approximate Nearest Neighbor Search—0
HCM: Hardware-Aware Complexity Metric for Neural Network Architectures—0
HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates—0
HDR Imaging With One-Bit Quantization—0
HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers—0
HEMP: High-order Entropy Minimization for neural network comPression—0
HEPPO: Hardware-Efficient Proximal Policy Optimization -- A Universal Pipelined Architecture for Generalized Advantage Estimation—0
HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference—0
QET: Enhancing Quantized LLM Parameters and KV cache Compression through Element Substitution and Residual Clustering—0
Heterogeneous quantization regularizes spiking neural network activity—0
Hexcute: A Tile-based Programming Language with Automatic Layout and Task-Mapping Synthesis—0
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask—0
Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement—0
Hierarchical Modeling of Multidimensional Data in Regularly Decomposed Spaces: Synthesis and Perspective—0
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design—0
High-Accuracy Inference in Neuromorphic Circuits using Hardware-Aware Training—0
High-contrast, speckle-free, true 3D holography via binary CGH optimization—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