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 3851–3875 of 4925 papers

TitleStatusHype
Power-Efficient Sampling—0
Power Measurement Enabled Channel Autocorrelation Matrix Estimation for IRS-Assisted Wireless Communication—0
Power-of-Two (PoT) Weights in Large Language Models (LLMs)—0
Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks—0
PowerQuant: Automorphism Search for Non-Uniform Quantization—0
PQCache: Product Quantization-based KVCache for Long Context LLM Inference—0
PQD: Post-training Quantization for Efficient Diffusion Models—0
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation—0
PQTable: Fast Exact Asymmetric Distance Neighbor Search for Product Quantization Using Hash Tables—0
PQTable: Non-exhaustive Fast Search for Product-quantized Codes using Hash Tables—0
Practical cognitive speech compression—0
Practical Data-Dependent Metric Compression with Provable Guarantees—0
Practical Locally Private Federated Learning with Communication Efficiency—0
Practical Modulo Sampling: Mitigating High-Frequency Components—0
PR-CIM: a Variation-Aware Binary-Neural-Network Framework for Process-Resilient Computation-in-memory—0
Precipitation Nowcasting Using Physics Informed Discriminator Generative Models—0
Precision and Recall Reject Curves for Classification—0
Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference—0
Precision Enhancement of 3D Surfaces from Multiple Compressed Depth Maps—0
Precision Highway for Ultra Low-Precision Quantization—0
Precision Neural Network Quantization via Learnable Adaptive Modules—0
Precision Where It Matters: A Novel Spike Aware Mixed-Precision Quantization Strategy for LLaMA-based Language Models—0
Precoding Design for Limited-Feedback MISO Systems via Character-Polynomial Codes—0
Predicting Attention Sparsity in Transformers—0
Predicting Attention Sparsity in Transformers—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