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 4276–4300 of 4925 papers

TitleStatusHype
Accelerating PoT Quantization on Edge DevicesCode0
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex HullsCode0
QSGD: Communication-Efficient SGD via Gradient Quantization and EncodingCode0
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth PredictionCode0
MorpheusNet: Resource efficient sleep stage classifier for embedded on-line systemsCode0
TinySubNets: An efficient and low capacity continual learning strategyCode0
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural NetworksCode0
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete DiffusionCode0
AlignedKV: Reducing Memory Access of KV-Cache with Precision-Aligned QuantizationCode0
Mirror Descent View for Neural Network QuantizationCode0
MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural NetworksCode0
Minimize Quantization Output Error with Bias CompensationCode0
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local ComputationsCode0
Minimal Random Code Learning: Getting Bits Back from Compressed Model ParametersCode0
TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference Acceleration Using Gradient Boosted Decision TreesCode0
MetaAug: Meta-Data Augmentation for Post-Training QuantizationCode0
BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized WeightsCode0
Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM Safety AlignmentCode0
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial ExamplesCode0
FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal RetrievalCode0
AMED: Automatic Mixed-Precision Quantization for Edge DevicesCode0
Discrete representations in neural models of spoken languageCode0
QTTNet: Quantized Tensor Train Neural Networks for 3D Object and Video Recognition.Code0
GLAD: Improving Latent Graph Generative Modeling with Simple QuantizationCode0
Discrete Factorization Machines for Fast Feature-based RecommendationCode0
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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