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 4501–4525 of 4925 papers

TitleStatusHype
Quantizing deep convolutional networks for efficient inference: A whitepaperCode0
A2Q: Accumulator-Aware Quantization with Guaranteed Overflow AvoidanceCode0
Deep residual network for steganalysis of digital imagesCode0
LegalEval-Q: A New Benchmark for The Quality Evaluation of LLM-Generated Legal TextCode0
Ultrafast jet classification on FPGAs for the HL-LHCCode0
Large Scale Clustering with Variational EM for Gaussian Mixture ModelsCode0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural NetworksCode0
AdaBits: Neural Network Quantization with Adaptive Bit-WidthsCode0
Bit Error Robustness for Energy-Efficient DNN AcceleratorsCode0
Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase OptimizationCode0
Communication-Efficient Distributed Blockwise Momentum SGD with Error-FeedbackCode0
Deep Recurrent Quantization for Generating Sequential Binary CodesCode0
Exact Backpropagation in Binary Weighted Networks with Group Weight TransformationsCode0
Automated Cancer Subtyping via Vector Quantization Mutual Information MaximizationCode0
Deep Priority HashingCode0
On Quantizing Neural Representation for Variable-Rate Video CodingCode0
Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspectiveCode0
Evaluating Quantized Large Language Models for Code Generation on Low-Resource Language BenchmarksCode0
On Resource-Efficient Bayesian Network Classifiers and Deep Neural NetworksCode0
Communication-Censored Distributed Stochastic Gradient DescentCode0
Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the EdgeCode0
Evaluating Large Language Models on the Frame and Symbol Grounding Problems: A Zero-shot BenchmarkCode0
Deep Optimized Multiple Description Image Coding via Scalar Quantization LearningCode0
SHE: A Fast and Accurate Deep Neural Network for Encrypted DataCode0
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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