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 3451–3475 of 4925 papers

TitleStatusHype
Kramers-Kronig Receiver Combined With Digital Resolution Enhancer—0
CREW: Computation Reuse and Efficient Weight Storage for Hardware-accelerated MLPs and RNNs—0
DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms—0
Support Recovery in Universal One-bit Compressed Sensing—0
A High-Performance Adaptive Quantization Approach for Edge CNN Applications—0
Deep Learning to Ternary Hash Codes by Continuation—0
Continuous-variable neural-network quantum states and the quantum rotor modelCode0
MAFAT: Memory-Aware Fusing and Tiling of Neural Networks for Accelerated Edge Inference—0
Efficient Approximate Search for Sets of Vectors—0
HEMP: High-order Entropy Minimization for neural network comPression—0
LANA: Latency Aware Network Acceleration—0
Regularized Classification-Aware QuantizationCode0
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions—0
Image restoration quality assessment based on regional differential information entropy—0
An Embedded Iris Recognition System Optimization using Dynamically ReconfigurableDecoder with LDPC Codes—0
Patch-Wise Spatial-Temporal Quality Enhancement for HEVC Compressed VideoCode0
Discrete-Valued Neural Communication—0
Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks—0
Q-SpiNN: A Framework for Quantizing Spiking Neural Networks—0
Multi-modality Deep Restoration of Extremely Compressed Face Videos—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Exact Backpropagation in Binary Weighted Networks with Group Weight TransformationsCode0
Orthonormal Product Quantization Network for Scalable Face Image RetrievalCode0
Power Law Graph Transformer for Machine Translation and Representation LearningCode0
Post-Training Quantization for Vision Transformer—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