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 951–975 of 4925 papers

TitleStatusHype
Deep activity propagation via weight initialization in spiking neural networks—0
Trainable pruned ternary quantization for medical signal classification modelsCode0
Quantized and Asynchronous Federated Learning—0
Constraint Guided Model Quantization of Neural Networks—0
Mixed-Precision Embeddings for Large-Scale Recommendation Models—0
Accelerating PoT Quantization on Edge DevicesCode0
Aggressive Post-Training Compression on Extremely Large Language Models—0
Rotated Runtime Smooth: Training-Free Activation Smoother for accurate INT4 inference—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelCode0
Asymptotic tracking control of dynamic reference over homomorphically encrypted data with finite modulus—0
Heterogeneous quantization regularizes spiking neural network activity—0
A method of using RSVD in residual calculation of LowBit GEMM—0
Fronthaul-Constrained Distributed Radar Sensing—0
Digital and Hybrid Precoding Designs in Massive MIMO with Low-Resolution ADCsCode0
Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior ModelsCode0
MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling—0
Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores—0
P4Q: Learning to Prompt for Quantization in Visual-language Models—0
Using Random Codebooks for Audio Neural AutoEncoders—0
Reinforcement Learning for Finite Space Mean-Field Type Games—0
Search for Efficient Large Language ModelsCode1
AlignedKV: Reducing Memory Access of KV-Cache with Precision-Aligned QuantizationCode0
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms—0
Accumulator-Aware Post-Training Quantization—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