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 1651–1675 of 4925 papers

TitleStatusHype
Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training—0
Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality GapCode0
BiLLM: Pushing the Limit of Post-Training Quantization for LLMsCode3
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice CodebooksCode4
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes—0
QuEST: Low-bit Diffusion Model Quantization via Efficient Selective FinetuningCode2
A Survey on Transformer Compression—0
Quantized Approximately Orthogonal Recurrent Neural Networks—0
KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV CacheCode3
Optimal and Near-Optimal Adaptive Vector Quantization—0
FoldToken: Learning Protein Language via Vector Quantization and Beyond—0
Stability Analysis of Various Symbolic Rule Extraction Methods from Recurrent Neural Network—0
Leveraging Continuously Differentiable Activation Functions for Learning in Quantized Noisy EnvironmentsCode0
LQER: Low-Rank Quantization Error Reconstruction for LLMsCode1
Locally-Adaptive Quantization for Streaming Vector Search—0
Ultrafast jet classification on FPGAs for the HL-LHCCode0
Improved Quantization Strategies for Managing Heavy-tailed Gradients in Distributed Learning—0
Large Language Models for Time Series: A SurveyCode4
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation—0
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign DecodingCode0
Truncated Non-Uniform Quantization for Distributed SGD—0
HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays—0
Neural Language of Thought Models—0
Faster Inference of Integer SWIN Transformer by Removing the GELU Activation—0
An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec—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