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 3276–3300 of 4925 papers

TitleStatusHype
DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization—0
High-Dimensional Distribution Generation Through Deep Neural Networks—0
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification—0
Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems—0
Finite-Bit Quantization For Distributed Algorithms With Linear Convergence—0
Pruning Ternary Quantization—0
Kramers-Kronig Receiver Combined With Digital Resolution Enhancer—0
HARP-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding—0
CREW: Computation Reuse and Efficient Weight Storage for Hardware-accelerated MLPs and RNNs—0
Support Recovery in Universal One-bit Compressed Sensing—0
DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms—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
Regularized Classification-Aware QuantizationCode0
LANA: Latency Aware Network Acceleration—0
HEMP: High-order Entropy Minimization for neural network comPression—0
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions—0
Patch-Wise Spatial-Temporal Quality Enhancement for HEVC Compressed VideoCode0
An Embedded Iris Recognition System Optimization using Dynamically ReconfigurableDecoder with LDPC Codes—0
Image restoration quality assessment based on regional differential information entropy—0
SimCC: a Simple Coordinate Classification Perspective for Human Pose EstimationCode1
Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks—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