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 2851–2875 of 4925 papers

TitleStatusHype
RQP-SGD: Differential Private Machine Learning through Noisy SGD and Randomized Quantization—0
RTF-Q: Efficient Unsupervised Domain Adaptation with Retraining-free Quantization—0
RTN: Reparameterized Ternary Network—0
RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content—0
Running Conventional Automatic Speech Recognition on Memristor Hardware: A Simulated Approach—0
S3D: A Simple and Cost-Effective Self-Speculative Decoding Scheme for Low-Memory GPUs—0
S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation—0
S4: a High-sparsity, High-performance AI Accelerator—0
SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inference—0
SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG—0
Saliency Assisted Quantization for Neural Networks—0
HDR image watermarking using saliency detection and quantization index modulation—0
SAMP: A Model Inference Toolkit of Post-Training Quantization for Text Processing via Self-Adaptive Mixed-Precision—0
Sampled-data control design for systems with quantized actuators—0
Sampling From Autoencoders' Latent Space via Quantization And Probability Mass Function Concepts—0
Sampling Streaming Data with Parallel Vector Quantization -- PVQ—0
SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model—0
Scalable and consistent embedding of probability measures into Hilbert spaces via measure quantization—0
Scalable and Efficient Neural Speech Coding: A Hybrid Design—0
Scalable Image Retrieval by Sparse Product Quantization—0
Scalable Multivariate Fronthaul Quantization for Cell-Free Massive MIMO—0
Scalable Nearest Neighbor Search based on kNN Graph—0
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization—0
Scalable Representation Learning for Multimodal Tabular Transactions—0
Scalable Thermodynamic Second-order 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