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 1826–1850 of 4925 papers

TitleStatusHype
FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks—0
DQ-Data2vec: Decoupling Quantization for Multilingual Speech Recognition—0
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance—0
FlattenQuant: Breaking Through the Inference Compute-bound for Large Language Models with Per-tensor Quantization—0
ASER: Activation Smoothing and Error Reconstruction for Large Language Model Quantization—0
BitNet b1.58 Reloaded: State-of-the-art Performance Also on Smaller Networks—0
DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations—0
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming—0
FleXOR: Trainable Fractional Quantization—0
FlexQuant: Elastic Quantization Framework for Locally Hosted LLM on Edge Devices—0
A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks—0
FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs—0
FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference—0
A new heuristic algorithm for fast k-segmentation—0
Compression for Better: A General and Stable Lossless Compression Framework—0
Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training—0
FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization—0
Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test—0
FlowVQTalker: High-Quality Emotional Talking Face Generation through Normalizing Flow and Quantization—0
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates—0
FoldToken2: Learning compact, invariant and generative protein structure language—0
FoldToken: Learning Protein Language via Vector Quantization and Beyond—0
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks—0
Forearm Ultrasound based Gesture Recognition on Edge—0
DP-Net: Dynamic Programming Guided Deep Neural Network Compression—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