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 126–150 of 4925 papers

TitleStatusHype
FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization—0
Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing—0
Mind the Gap: A Practical Attack on GGUF QuantizationCode1
Distinctive Feature Codec: Adaptive Segmentation for Efficient Speech Representation—0
LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-TuningCode0
Adaptive Prediction-Powered AutoEval with Reliability and Efficiency GuaranteesCode0
Reducing Storage of Pretrained Neural Networks by Rate-Constrained Quantization and Entropy CodingCode0
DVD-Quant: Data-free Video Diffusion Transformers QuantizationCode1
PM-KVQ: Progressive Mixed-precision KV Cache Quantization for Long-CoT LLMsCode1
NeUQI: Near-Optimal Uniform Quantization Parameter InitializationCode0
Beyond Discreteness: Finite-Sample Analysis of Straight-Through Estimator for Quantization—0
NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache—0
Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM—0
Task Specific Pruning with LLM-Sieve: How Many Parameters Does Your Task Really Need?—0
UniTTS: An end-to-end TTS system without decoupling of acoustic and semantic informationCode1
FPQVAR: Floating Point Quantization for Visual Autoregressive Model with FPGA Hardware Co-designCode0
NQKV: A KV Cache Quantization Scheme Based on Normal Distribution Characteristics—0
DuFFin: A Dual-Level Fingerprinting Framework for LLMs IP ProtectionCode0
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing—0
Is (Selective) Round-To-Nearest Quantization All You Need?—0
Segmentation-Variant Codebooks for Preservation of Paralinguistic and Prosodic Information—0
Harnessing Large Language Models Locally: Empirical Results and Implications for AI PCCode0
Rate-Distortion Optimization with Non-Reference Metrics for UGC Compression—0
InTreeger: An End-to-End Framework for Integer-Only Decision Tree Inference—0
Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability HypothesisCode1
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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