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 3651–3675 of 4925 papers

TitleStatusHype
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!—0
Analytical aspects of non-differentiable neural networks—0
High Performance Natural Language Processing—0
A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings—0
Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling DecoderCode1
Time regularization as a solution to mitigate quantization induced performance degradation—0
One-Bit Direct Position Determination of Narrowband Gaussian Signals—0
A Greedy Algorithm for Quantizing Neural NetworksCode1
Accordion: Adaptive Gradient Communication via Critical Learning Regime IdentificationCode1
Permute, Quantize, and Fine-tune: Efficient Compression of Neural NetworksCode1
Enhanced Blind Calibration of Uniform Linear Arrays with One-Bit Quantization by Kullback-Leibler Divergence Covariance Fitting—0
Diagnostic data integration using deep neural networks for real-time plasma analysis—0
INT8 Winograd Acceleration for Conv1D Equipped ASR Models Deployed on Mobile Devices—0
Full-Duplex Cell-Free mMIMO Systems: Analysis and Decentralized Optimization—0
FD Cell-Free mMIMO: Analysis and Optimization—0
A Statistical Framework for Low-bitwidth Training of Deep Neural NetworksCode1
A QP-adaptive Mechanism for CNN-based Filter in Video Coding—0
MARS: Multi-macro Architecture SRAM CIM-Based Accelerator with Co-designed Compressed Neural Networks—0
ShiftAddNet: A Hardware-Inspired Deep NetworkCode1
Millimeter Wave MIMO Channel Estimation with 1-bit Spatial Sigma-delta Analog-to-Digital Converters—0
Linearly Converging Error Compensated SGDCode0
Adaptive Gradient Quantization for Data-Parallel SGDCode1
On Resource-Efficient Bayesian Network Classifiers and Deep Neural NetworksCode0
Recovery of sparse linear classifiers from mixture of responses—0
Mixed-Precision Embedding Using a Cache—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